International Journal of Innovative Research in Computer and Communication Engineering, 2014
Clustering can be defined as the process of partitioning a set of patterns into disjoint and homo... more Clustering can be defined as the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups, called clusters. The growing need for distributed clustering algorithms is attributed to the huge size of databases that is common nowadays. The task of extracting knowledge from large databases, in the form of clustering rules, has attracted considerable attention. Distributed clustering algorithms embrace this trend of merging computations with communication and explore all the facets of the distributed computing environments. Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. An important feature of the proposed technique is that it is able to automatically find the optimal number of clusters (i.e., the number of clusters does not have to be known in advance) even for very high dimensional data sets, where tracking of the number of clusters may be highly impossible. The proposed Optimal Associative Clustering algorithm using genetic algorithm and bayes factor for precision is able to outperform two other state-of-the-art clustering algorithms in a statistically meaningful way over a majority of the benchmark data sets. The result of the proposed optimal associative clustering algorithm is compared with one existing algorithm on two multi dimensional datasets. Experimental result demonstrates that the proposed method is able to achieve a better clustering solution when compared with existing algorithms.
Journal of Computational Mechanics, Power System and Control, 2021
Generally, Indian Classical Music (ICM) is categorized into 2 Hindustani and Carnatic. Although, ... more Generally, Indian Classical Music (ICM) is categorized into 2 Hindustani and Carnatic. Although, aforesaid music formats possess the same base, the presentation manner is different in numerous ways. The ICM basic modules are taala and raga. Fundamentally, Taala indicates rhythmic beats else patterns. From the flow of swaras, the raga is ascertained that is indicated as extensive terms. On the basis of few important factors, the raga is indicated namely aarohana-avarohna and swaras and distinctive phrases. In practice, the basic frequency is Swara that is exact via time period. In addition, there are numerous other issues with the automatic raga identification technique. Hence, raga is identified in this research without using precise note series information and vital to use an effectual classification technique. This research develops an effectual raga recognition model via the Carnatic genre music that is efficiently identified for the data mining models, which is also included. Here, the Neural Network (NN) model is proposed which is an adaptive classifier in that the feature set is exploited to learning, and the data mining models are used for the classification techniques. Moreover, a meta-heuristic approach is used for the adaptive classifier to obtain the extracted feature set knowledge. As the learning technique plays an important role in describing the accuracy of the raga identification model, it prefers to adopt the Dragonfly algorithm.
To address the clustering problem related to multi-dimensional data clustering, a number of techn... more To address the clustering problem related to multi-dimensional data clustering, a number of techniques have been implemented. A constraint based multi-dimensional data-clustering algorithm is proposed in this paper which helped with associative clustering can find out the number of clusters optimally present in a multi-dimensional data set. Now, by bays factor computation process associative constraint based clustering process is executed. Moreover, genetic algorithm is applied to optimization process to discover the optimal cluster results. The constraints based proposed algorithm assists in recognizing the right data to be clustered and the knowledge considering the data regarded as a constraint which enhances the precision of clustering. The data constraints furthermore assist in indicating the data related to the clustering task. The result of the proposed optimal associative clustering algorithm is compared with an existing algorithm on two multi dimensional datasets. Experimen...
With the growth of web, people are using it as a medium for expressing their opinions, thoughts t... more With the growth of web, people are using it as a medium for expressing their opinions, thoughts through blog posts, reviews (in the form of ratings), and forums. Blogosphere is a place where people read, write their views and make comments on others views or thoughts there by exchanging information. It will be very difficult for any business, organization or individual to go through and understand thoughts expressed by others on a product or topic which they are interested in. Hence a summarization system which extracts, analyze and summarize opinions will be useful. Our Summarization system exactly does the same for blog posts. The entire process of summary generation is done in three stages, extract sentences which are sources for opinion (Opinion Mining), then analyze the extracted opinions to determine polarity (Opinion Analysis) and finally ranking the opinion sentences (Opinion Summarization). We present a classification based approach for extracting and analyzing opinions and how we use this approach to rank sentences for generating summary.
With the growth of web, people are using it as a medium for expressing their opinions, thoughts t... more With the growth of web, people are using it as a medium for expressing their opinions, thoughts through blog posts, reviews (in the form of ratings), and forums. Blogosphere is a place where people read, write their views and make comments on others views or thoughts there by exchanging information. It will be very difficult for any business, organization or individual to go through and understand thoughts expressed by others on a product or topic which they are interested in. Hence a summarization system which extracts, analyze and summarize opinions will be useful. Our Summarization system exactly does the same for blog posts. The entire process of summary generation is done in three stages, extract sentences which are sources for opinion (Opinion Mining), then analyze the extracted opinions to determine polarity (Opinion Analysis) and finally ranking the opinion sentences (Opinion Summarization). We present a classification based approach for extracting and analyzing opinions and how we use this approach to rank sentences for generating summary.
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Papers by Kranthi Kiran