Rough Sets

This paper presents a general framework for the study of fuzzy rough sets in which both constructive and axiomatic approaches are used. In constructive approach, a pair of lower and upper generalized approximation operators is defined. The connections between fuzzy relations and fuzzy rough approximation operators are examined. In axiomatic approach, various classes of fuzzy rough approximation operators are characterized by different sets of axioms. Axioms of fuzzy approximation operators guarantee the existence of certain types of fuzzy relations producing the same operators.

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

Prediction of consumer demands is a pre-requisite for optimal control of water distribution systems because minimum-cost pumping schedules can be computed if water demands are accurately estimated This paper presents an enhanced rough-sets method for generating prediction rules from a set of observed data. The proposed method extends upon the standard rough set model by making use of the statistical information inherent in the data to handle incomplete and ambiguous training samples. It also discusses some experimental results from using this method for discovering knowledge on water demand prediction.

We introduce a multi-spectral MRI segmentation technique based on approximate reducts derived from the data mining paradigm of the theory of rough sets. We use genetic algorithms to tune the parameterized attributes and search for the best segmentation models based on approximate reducts.

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information -coming from microscopic examinations and subjective remarks -has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input-output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods -rough set theory and artificial neural networks, mainly -reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics. ᭧

We discuss information granule calculi as a basis of granular computing. They are defined by constructs like information granules, basic relations of inclusion and closeness between information granules as well as operations on them. The exact interpretation between granule languages of different information sources (agents) often does not exist. Hence (rough) inclusion and closeness of granules are considered instead of their equality. Examples of all the basic constructs of information granule calculi are presented. The construction of more complex information granules is described by expressions called terms. We discuss the synthesis problem of robust terms, i.e., descriptions of information granules, satisfying a given specification in a satisfactory degree. We also present a method for synthesis of information granules represented by robust terms (approximate schemes of reasoning) by means of decomposition of specifications for such granules. The discussed problems of granular computing are of special importance for many applications, in particular related to spatial reasoning as well as to knowledge discovery and data mining.

The present work proposes new styles of rough sets by using different neighborhoods which are made from a general binary relation. The proposed approximations represent a generalization to Pawlak's rough sets and some of its generalizations, where the accuracy of these approximations is enhanced significantly. Comparisons are obtained between the methods proposed and the previous ones. Moreover, we extend the notion of "nano-topology", which have introduced by Thivagar and Richard [49], to any binary relation. Besides, to demonstrate the importance of the suggested approaches for deciding on an effective tool for diagnosing lung cancer diseases, we include a medical application of lung cancer disease to identify the most risk factors for this disease and help the doctor in decision-making. Finally, two algorithms are given for decision-making problems. These algorithms are tested on hypothetical data for comparison with already existing methods.

Brighthouse is a column-oriented data warehouse with an automatically tuned, ultra small overhead metadata layer called Knowledge Grid, that is used as an alternative to classical indexes. The advantages of column-oriented data storage, as well as data compression have already been well-documented, especially in the context of analytic, decision support querying. This paper demonstrates additional benefits resulting from Knowledge Grid for compressed, column-oriented databases. In particular, we explain how it assists in query optimization and execution, by minimizing the need of data reads and data decompression.

Power generation today is an increasingly demanding task, worldwide, because of emphasis on efficient ways of generation. A power station is a complicated multivariable controlled plant, which consists of boiler, turbine, generator, power network and loads. The power sector sustainability depends on innovative technology and practices in maintaining unit performance, operation, flexibility and availability . The demands being placed on Control & Instrumentation engineers include economic optimization, practical methods for adaptive and learning control, software tools that place state-of-art methods . As a result, Fuzzy techniques are explored which aim to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low cost. This paper proposes use of fuzzy techniques in two critical areas of Soot Blowing optimization and Drum Level Control. Presently, in most of the Power stations the soot blowing is done based on a fixed time schedule. In many instances, certain boiler stages are blown unnecessarily, resulting in efficiency loss. Therefore an fuzzy based system is proposed which shall indicate individual section cleanliness to determine correct soot blowing scheme. Practical soot blowing optimization improves boiler performance, reduces NOx emissions and minimizes disturbances caused by soot blower activation. Due to the dynamic behaviour of power plant, controlling the Drum Level is critical. If the level becomes too low, the boiler can run dry resulting in mechanical damage of the drum and boiler tubes. If the level becomes too high, water can be carried over into the Steam Turbine which shall result in catastrophic damage. Therefore an fuzzy based system is proposed to replace the existing conventional controllers

In this chapter we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. A section is devoted to summarizing the state of rough sets as related to data mining of real-world databases.

Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became also a crucial issue for computer scientists, particularly in the area of artificial intelligence. Rough set theory proposed by Zdzisław Pawlak, in the early 1980's, presents still another attempt to this problem. The theory has attracted attention of many researchers and practitioners all over the world, who contributed essentially to its development and applications. Rough set theory has found many interesting applications. The rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning and pattern recognition.

Data mining is an interdisciplinary research area spanning severals disciplines such as database systems, machine learning, intelligent information systems, statistics, and expert systems. Data mining has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Many aspects of data mining have been investigated in several related elds. A unique but important aspect of the problem lies in the signi cance of needs to extend these studies to include the nature of the contents of the real-world databases. In this chapter, we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. Since a major focus of this book is on rough sets and its applications to database mining, one full section is devoted to summarize the state of rough sets as related to data mining of real-world databases. More importantly we believe that theory of rough sets constitutes a sound basis for data mining applications.

Considering the generalised approximation space, this paper aims to introduce and study eight approaches for approximating rough sets. The approximations are based on different topologies. Comparisons between the accuracy of these types of approximations are superimposed. Some fundamental properties of the suggested approximation operators are obtained.

Identifying spatio-temporal synchrony in a complex, interacting and oscillatory coupled-system is a challenge. In particular, the characterization of statistical relationships between environmental or biophysical variables with the multivariate data of pandemic is a difficult process because of the intrinsic variability and non-stationary nature of the time-series in space and time. This paper presents a methodology to address these issues by examining the bivariate relationship between Covid-19 and temperature time-series in the time-localized frequency domain by using Singular Value Decomposition (SVD) and continuous cross-wavelet analysis. First, the dominant spatio-temporal trends are derived by using the eigen decomposition of SVD. The Covid-19 incidence data and the temperature data of the corresponding period are transformed into significant eigen-state vectors for each spatial unit. The Morlet Wavelet transformation is performed to analyse and compare the frequency structure of the dominant trends derived by the SVD. The result provides cross-wavelet transform and wavelet coherence measures in the ranges of time period for the corresponding spatial units. Additionally, wavelet power spectrum and paired wavelet coherence statistics and phase difference are estimated. The result suggests statistically significant coherency at various frequencies providing insight into spatio-temporal dynamics. Moreover, it provides information about the complex conjugate dynamic relationships in terms phases and phase differences.

This paper is concerned with solving large scale linear fractional multiple objective programming (LSLFMOP) problems with chance constraints. Channce constraints involve random parameters in the right-hand sides. These random right-hand sides are considered to be statistically independent random variable. The main features of the proposed solution procedure are based on chanceconstrained technique, Charens and Cooper transformation and the Rosen's partitioning procedure. An illustrative numerical example is given to clarify the main results developed in the paper.

3rd International Conference on Soft Computing, Artificial Intelligence and Machine Learning (SAIM 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing, Machine Learning.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.

Soft computing is likely to play an important role in science and engineering in the future. The
successful applications of soft computing and the rapid growth suggest that the impact of soft
computing will be felt increasingly in coming years. Soft Computing encourages the integration
of soft computing techniques and tools into both everyday and advanced applications. This
journal serves as a platform that fosters new applications for all scientists and engineers engaged
in research and development in this fast growing field

Data mining is an interdisciplinary research area spanning severals disciplines such as database systems, machine learning, intelligent information systems, statistics, and expert systems. Data mining has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Many aspects of data mining have been investigated in several related elds. A unique but important aspect of the problem lies in the signiicance of needs to extend these studies to include the nature of the contents of the real-world databases. In this chapter, we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. Since a major focus of this book is on rough sets and its applications to database mining, one full section is devoted to summari...

This paper proposes a definition of a fuzzy partition element based on the homomorphism between type-1 fuzzy sets and the three-valued Kleene algebra. A new clustering method based on the C-means algorithm, using the defined partition, is presented in this paper, which will be validated with the traditional iris clustering problem by measuring its petals.

We provide a scheme for the synchronization of two chaotic mobile robots when a mismatch between the parameter values of the systems to be synchronized is present. We have shown how meta-heuristic optimization can be used to adapt the parameters in two coupled systems such that the two systems are synchronized, although their behavior is chaotic and they have started with different initial conditions and parameter settings. The controlled system synchronizes its dynamics with the control signal in the periodic as well as chaotic regimes. The method can be seen also as another way of controlling the chaotic behavior of a coupled system. In the case of coupled chaotic systems, under the interaction between them, their chaotic dynamics can be cooperatively self-organized. A synergistic approach to meta-heuristic optimization search algorithm is developed. To avoid being trapped into local optimum and to enrich the searching behavior, chaotic dynamics is incorporated into the proposed search algorithm. A chaotic Levy flight is firstly incorporated in the proposed search algorithm for efficiently generating new solutions. And secondly, chaotic sequence and a psychology factor of emotion are introduced for move acceptance in the search algorithm. We illustrate the application of the algorithm by estimating the complete parameter vector of a chaotic mobile robot.

We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised clustering and the rules derived from approximate decision reducts. We utilize the MRI phantoms from the Simulated Brain Database. We run experiments on randomly selected slices from a volumetric set of multi-modal MR images (T1, T2, PD). Segmentation accuracy reaches 96% for the highest resolution images and 89% for the noisiest image volume. We also tested the resultant classifier on real clinical data, which yielded an accuracy of approximately 84%.

In virtualization based system load balancing and migration enabled consolidation or dispersal
of virtual machine (VM) is an important issue. The decision of consolidation or dispersal is
taken depending upon the type and amount of load in VMs. Clustering of VMs depends on the
type of loads is a major issue in load balancing of VMs. Analysis of different loads and the
resource usage by them is a major part of this work. For each of the resources there are many
parameters are present to describe the usage of it. Different feature selection algorithms has
been used for analysis of the data and to reduce the dimensionality of the data. The clustering
techniques are mainly applied on this analysed data. K-means and EM algorithm has been used
for clustering of the VMs. The goodness of the clusters has been determined using Davies-
Bouldin index and Dunn index.

In this paper an attempt has been made to review the research studies on application of data mining techniques in the field of agriculture. Some of the techniques, such asID3 algorithms, the k-means, the k nearest neighbour, artificial neural networks and support vector machines applied in the field of agriculture were presented. Data mining in application in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. This article explores the applications of data mining techniques in the field of agriculture and allied sciences.

The NAC gene family encodes a large family of plant-specific transcription factors with diverse roles in various developmental processes and stress responses in plants. Creation of genome wide prediction tools for NAC proteins will have a significant impact on gene annotation in rice. In the present study, NACSVM, a tool for computational genome-scale prediction of NAC proteins in rice was developed integrating compositional and evolutionary information of NAC proteins. Initially, support vector machine (SVM)based modules were developed using combinatorial presence of diverse protein features such as traditional amino acid, dipeptide (i+1), tripeptide (i+2), four-parts composition and PSSM and an overall accuracy of 79%, 93%, 93%, 79% and 100% respectively was achieved. Later, two hybrid modules were developed based on amino acid, dipeptide and tripeptide composition, through which an overall accuracy of 83% and 79% was achieved. NACSVM was also evaluated using position-specific iterated-basic local alignment search tool which resulted in a lower accuracy of 50%. In order to benchmark NACSVM , the tool was evaluated using independent data test and cross validation methods. The different statistical analyses carried out revealed that the proposed algorithm is an useful tool for annotating NAC proteins in genome of rice.

A new methodology is developed to analyse existing water quality monitoring networks. This methodology incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy reduction. The work starts with a formulation of a conceptual framework for groundwater quality monitoring to represent the methodology's context. This work presents the development of Bayesian techniques for the assessment of groundwater quality. The primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes' theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.

Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough setbased research directions and applications.

This paper presents three-level quadratic programming problem with random rough coefficient in constraints. At the
first phase of the solution algorithm and to avoid the complexity of this problem, we begin with converting the rough
nature in constraints into equivalent crisp using intervals technique. At the second phase, a membership function is
constructed to develop a fuzzy model for obtaining a compromise solution of the three-level quadratic programming
problem. In addition, the theoretical results are illustrated with the help of a numerical example.

An alternative numerical method for solving contact problems for real rough surfaces is proposed. The real area of contact and the contact pressure distribution are determined using a single-loop iteration scheme based on the conjugate gradient method, which converges for arbitrary rough surfaces. The surface deflections and subsurface stresses are computed using an alternative two-dimensional multi-level multi-summation algorithm, which allows the summation error to be kept under the discretization error for any number of contact points. The proposed method is fast: rough contact problems for surface samples with 10 5 -10 6 data points are solved on a personal computer in a few hours. The numerical algorithms are described in full detail so that an interested reader can implement the new contact solver in a computer code. Numerical examples demonstrating the method advantages are presented. The method is compared with other fast contact solvers that have emerged in the last few years. q 1999 Elsevier Science S.A. All rights reserved.

Considering the generalised approximation space, this paper aims to introduce and study eight approaches for approximating rough sets. The approximations are based on different topologies. Comparisons between the accuracy of these types of approximations are superimposed. Some
fundamental properties of the suggested approximation operators are obtained.

This paper presents an approach in analyzing personality types, temperament and team diversity to determine software engineering (SE) teams performance. The benefits of understanding personality types and its relationships amongst team members are crucial for project success. Rough set analysis was used to analyze Myers-Briggs Type Indicator (MBTI) personality types, Keirsey temperament, team diversity, and team performance. The result shows positive relationships between these attributes.

This paper can be viewed as a generalization of Pawlak approximation space using general topological structure. Our
approach depends on a general topology generated by binary relation. The introduced technique is useful because the
concepts and the properties of the generated topology are applied on rough set theory and this open the way for more
topological applications in rough context. Several properties and examples are provided.

Skin colour detection is frequently been used for searching people, face detection, pornographic filtering and hand tracking. The presence of skin or non-skin in digital image can be determined by manipulating pixels' colour and/or pixels' texture. The main problem in skin colour detection is to represent the skin colour distribution model that is invariant or least sensitive to changes in illumination condition. Another problem comes from the fact that many objects in the real world may possess almost similar skin-tone colour such as wood, leather, skin-coloured clothing, hair and sand. Moreover, skin colour is different between races and can be different from a person to another, even with people of the same ethnicity. Finally, skin colour will appear a little different when different types of camera are used to capture the object or scene. The objective in this study is to develop a skin colour classifier based on pixel-based using RGB ratio model. The RGB ratio model is a newly proposed method that belongs under the category of an explicitly defined skin region model. This skin classifier was tested with SIdb dataset and two benchmark datasets; UChile and TDSD datasets to measure classifier performance. The performance of skin classifier was measured based on true positive (TF) and false positive (FP) indicator. This newly proposed model was compared with Kovac, Saleh and Swift models. The experimental results showed that the RGB ratio model outperformed all the other models in term of detection rate. The RGB ratio model is able to reduce FP detection that caused by reddish objects colour as well as be able to detect darkened skin and skin covered by shadow.

This paper attempts to explore the possibility of using sound signatures for vehicle detection and classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of horns, random but identifiable back ground noises, continuous high energy noises on the back ground are the different challenges encountered in the data collection. Different features were explored out of which smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency ceptral coefficients extracted from fixed regions around the detected peaks along with the manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to predict categories well.

We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA.

Gene selection is a main procedure of discriminate analysis of microarray data which is the process of selecting most informative genes from the whole gene data base. This paper approach a method for selecting informative genes by using Rough Set Theory. Rough Set Theory is a effective mathematical tool for selecting informative genes. This paper describes basics of Rough Set Theory and Rough Set attribute reduction by Quick -Reduct based Genetic Algorithm.

Types are a crucial concept in conceptual modelling, logic, and knowledge representation as they are an ubiquitous device to understand and formalise the classification of objects. We propose a logical treatment of types based on a cognitively inspired modelling that accounts for the amount of information that is actually available to a certain agent in the task of classification. We develop a predicative modal logic whose semantics is based on conceptual spaces that model the actual information that a cognitive agent has about objects, types, and the classification of an object under a certain type. In particular, we account for possible failures in the classification, for the lack of sufficient information, and for some aspects related to vagueness.

As a result of increased globalization, the Japanese public and corporations alike are beginning to interact more openly with Arabic corporate. The primary focus of this study is to shed light on and compare corporate logo designs of English alphabet type and product design (logos) images to those of Arabic alphabetic type in Japan. Factor analyses revealed three main images-" Traditional and Consistent, " " Familiar and Favorable, " and " Creative and Innovative "-and discovered that Arabic logotypes seems to be creative yet unfamiliar to Japan. Similarly cluster analyses for the factor scores of product logos indicated the same creativity and unfamiliarity in product logos. Moreover Rough sets analyses showed that corporate logos with English alphabetic logotypes were more familiar than those with Arabic alphabetic of the same corporate. Our findings suggest that in order to enhance Arabic corporate Logos in the Japanese marketplace, it is extremely important to familiarize Japanese with the Arabic alphabets.

Medical Data Mining is an emerging interest in the field of research. Medical Data is enormous and mining the required knowledge from the data is quite complex and interesting task. Data Mining is an intellectual tool, which is used to mine the required knowledge even from imprecise, uncertainty and noisy medical data. Sometimes reducing the unnecessary features will improve the accuracy of data. Many Feature Reduction Techniques are available. Heart disease is a group of condition affecting the structure and functions of the heart and has many root causes. Heart disease is the leading cause of death in all over the world in recent years. Researchers have developed many data mining techniques for diagnosing heart disease. This paper proposes an Intelligent Hybrid Quick Reduct Particle Swarm Optimization (IHQRPSO) Algorithm which hybrids the features of Particle Swarm Optimization and Quick Reduct Algorithm for Feature Reduction in Cardiac Disease Prediction. The proposed algorithm provided promising results which supports the algorithm in Feature Reduction. I. Introduction Feature selection is a method of eliminating features with little or no information. A large number of features with high dimension space will be large and unclean and degrade the classification accuracy [5]. In machine learning and statistic, the feature selection is considered as a problem of global combinatorial optimization [6]. Feature Selection techniques has three main advantages when constructing predictive models. The main benefits of the feature selection are that it improves the model interoperability, abridge the training time and over fitting which is for enhancing the generalization. A good feature selection mechanism is needed to amplify the processing rate with predictive exactitude and avoid incomprehensibility [7]. The problem size and the resultant search space for any learning algorithm can be reduced by using the Feature Selection. In general, two important goals of Feature Selection are to minimize the classification error and reduce the number of attributes [8]. There are number of Feature Selection algorithms available for Data Mining. The complicated task is to find the suitable algorithm which would select the feature relevant to the task without affecting the quality of the result. The Swarm Intelligent techniques and Rough Set Theory are the techniques that are mostly used for Feature reduction. But PSO is found to be effectual since it computes the individual best of each particle and finally compares all the individual particles to get the global best. It is also palpable that the Redunt feature obtained by PSO would not affect the accuracy. Rough Set also provides certain advantage in Feature reducing which does not affect the accuracy of the data set. Changes in the lifestyle, work culture and food habits lead to Cardio Vascular Disease (CVD) in developing countries. World Health Organization (WHO) reported that 30% of total global death is due to CVD [21, 22]. This estimation was done in 2008. It is also analyzed that the number of CVD Patients will increase from 300 to 600 million by 2020 [3]. By 2030, almost 25 million people will die from CVDs, mainly from heart disease and stroke [23]. Medical diagnosis is a process of decision making, during which the physician performs diagnosis of the new and unknown case from a set of clinical data from clinical experiences. This paper is organized as follows: Section II describes about Particle Swarm optimization algorithm with its pseudo-code. Section III describes about the Rough Set Theory and Section IV gives details about the literature that support PSO and RST in medical data. Section V describes the data set used. Section VI explains the proposed algorithms followed by Results and discussion in Section VII. Section VIII concludes the paper. II. Particle Swarm Optimization Particle Swarm Optimization (PSO) is based on the social behaviour associated with bird's flocking for optimization problem. A social behaviour pattern of organisms that live and interact within large groups is the inspiration for PSO [20, 21, 23]. The PSO is easier to lay into operation than Genetic Algorithm. It is for the motivation that PSO doesn't have mutation or crossover operators and movement of particles is effected by

This paper presents a new indiscernibility-based rough agglomerative hierarchical clustering algorithm for sequential data. In this approach, the indiscernibility relation has been extended to a tolerance relation with the transitivity property being relaxed. Initial clusters are formed using a similarity upper approximation. Subsequent clusters are formed using the concept of constrained-similarity upper approximation wherein a condition of relative similarity is used as a merging criterion. We report results of experimentation on msnbc web navigation dataset that are intrinsically sequential in nature. We have compared the results of the proposed approach with that of the traditional hierarchical clustering algorithm using vector coding of sequences. The results establish the viability of the proposed approach. The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group. Such descriptions can help web miners to identify potential and meaningful groups of users.

Medical diagnosis process vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases them
selves. Based on decision theory, in the past many mathematical models such as crisp set, probability distribution, fuzzy set, intuitionistic fuzzy set were developed to deal with complicating aspects of diagnosis. But, many such models are failed to include important aspects of the expert decisions. Therefore, an effort has been made to process inconsistencies in data being considered by Pawlak with the introduction of rough set theory. Though rough set has major advantages over the other methods, but it generates too many rules that create many difficulties while taking decisions. Therefore, it is essential to minimize the decision rules. In this paper, we use two processes such as preprocess and post process to mine suitable rules and to explore the relationship among the attributes. In pre process
we use rough set theory to mine suitable rules, whereas in post process we use formal concept analysis from these
suitable rules to explore better knowledge and most important factors affecting the decision making.

BharataNatyam (BN) is an ancient Indian Classical Dance (ICD). Creativity and in-
novation are the soul of any art including BN dance. Within the framework of rules
and traditionally accepted boundaries, choreographers try to innovate and create un-
seen, aesthetic and novel BN dance sequences. The human eorts can be supported with
the computational assistance to generate valid, genuine BN dance sequences. Moreover,
these movements can be empowered by the unspecied rules extracted from the analysis
of \Adavus", which are considered ideal BN dance sequences for \Nritta" or pure dance
movements. Thus an altogether new and interesting sequence can be obtained.
In this paper we present our experimental ArtToSMart (System Modeled art) sys-
tem, which gradually enhanced from one beat dance pose generation to `m' beat dance
sequences generation comprising of `m' system generated dance poses. Furthermore, we
are tagging these sequences with the help of BN dance experts and trying to develop a
machine learning model to classify system generated BN sequences. The use of Rough
Set tools have proved to be impressive for the same.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.