Papers by Hannah Inbarani
2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, 2013
Tagging is a popular way to annotate web 2.0 web sites. A tag is any user-generated word or phras... more Tagging is a popular way to annotate web 2.0 web sites. A tag is any user-generated word or phrase that helps to organize web 2.0 content. The current hype around web 2.0 applications, poses several important challenges for future data and web mining methods. An important challenge of Web 2.0 is the fact that a large amount of data has been generated over a short period. Clustering the tag data is very tedious since the tag space is very large in several social book marking web sites. So, instead of clustering the whole tag space of Web 2.0 data, some tags frequent enough in the tag space can be selected for clustering by applying feature selection techniques. The goal of feature selection is to determine a marginal bookmarked URL subset from a Web 2.0 data while retaining a suitably high accuracy in representing the original bookmarks. Tag clustering is the process of grouping similar tags into the same cluster and is important for the success of collaborative tagging services. In this paper, Unsupervised Quick Reduct feature selection algorithm is applied to find a set of most commonly tagged bookmarks and then clustering techniques such as Soft rough fuzzy clustering and Rough K-Means algorithms are applied for clustering of user generated tags and the performance of these clustering approaches are illustrated in this paper.
Studies in Big Data, 2015
The major challenge with Big Data analysis is the generation of huge amounts of data over a short... more The major challenge with Big Data analysis is the generation of huge amounts of data over a short period like Social tagging system. Social Tagging systems such as BibSonomy and del.icio.us have become progressively popular with the widespread use of the internet. The social tagging system is a popular way to annotate web 2.0 resources. Social tagging systems allow users to annotate web resources with free-form tags. Tags are widely used to interpret and classify the web 2.0 resources. Tag clustering is the process of grouping the similar tags into clusters. The tag clustering is very useful for searching and organizing the web2.0 resources and also important for the success of social tagging systems. Clustering the tag data is very tedious since the tag space is very large in several social bookmarking websites. So, instead of clustering the entire tag space of Web 2.0 data, some tags frequent enough in the tag space can be selected for clustering by applying feature selection techniques. The goal of feature selection is to determine a marginal bookmarked URL subset from Web 2.0 data while retaining a suitably high accuracy in representing the original bookmarks. In this chapter, Unsupervised Quick Reduct feature selection algorithm is applied to find a set of most commonly tagged bookmarks and this paper proposes TRS approach hybridized with Meta heuristic clustering algorithms. The proposed approaches are Hybrid TRS and K-Means Clustering (TRS-K-Means), Hybrid TRS and Particle swarm optimization (PSO) K-Means clustering algorithm (TRS-PSO-K-Means), and Hybrid TRS-PSO-K-Means-Genetic Algorithm (TRS-PSO-GA). These intelligent approaches automatically determine the number of clusters. These are in turn compared with K-Means benchmark algorithm for Social Tagging System.
Web Usage Mining make use of Association Rule Mining to discover the interesting pattern, identif... more Web Usage Mining make use of Association Rule Mining to discover the interesting pattern, identify web user behavior, predict web user expectation and improve the business strategy. Association Rule Mining is a technique of Data Mining which is used to find the relationship between the data items. In Web Usage Mining, data are stored in the web server in the form of web log files. Numerous amounts of website visitors visit the web sites. So, it is not easy to access the web log files and find the relationship among them because of the rapid growth of web log files. Some preprocessing works are needed to reduce the noisy data of web log files before applying the association rules to find the relationship between the log files. Many researchers done the variety of works on web content mining and web usage mining to improve the efficiency of the websites by providing novel methods and this paper gives an overview about the existing works done by the researchers on web usage mining.
Rough Set Based Feature Selection for Web Usage Mining
International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), 2007
... H.Hannah Inbarani, Department of Computer Science, Periyar University, Salem - 636 011, Tamil... more ... H.Hannah Inbarani, Department of Computer Science, Periyar University, Salem - 636 011, Tamil Nadu, India. hhinba@yahoo.co.in K.Thangavel, Department of Computer Science, PeriyarUniversity, Salem - 636 011, Tamil Nadu, India drktvelu@yahoo.com. ...
International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2013
Web personalisation systems include a new generation of recommender systems that integrate multip... more Web personalisation systems include a new generation of recommender systems that integrate multiple online channels, are more scalable, are more adaptive and can better handle user interactivity. Efficient and intelligent techniques from artificial intelligence, machine learning, web mining and statistics are needed to mine this data for actionable knowledge, and to effectively use the discovered knowledge to enhance the users' experience. In this paper, we propose a rough biclustering approach for creating user model, based on which user profiles are constructed and the user profiles are matched with the active user session for web page recommendation. To determine the effectiveness of the proposed approach, it is compared with conventional biclustering, spectral co-clustering and CDK-means biclustering using the evaluation metric used for page recommendation. The experimental results show that the proposed rough biclustering algorithm outperforms the other approaches for web page recommender systems.
PSORR - An Unsupervised Feature Selection Technique for Fetal Heart Rate
Fetal heart activity is generally monitored using a CardioTocoGraph (CTG) which estimates the fet... more Fetal heart activity is generally monitored using a CardioTocoGraph (CTG) which estimates the fetal tachogram based on the evaluation of ultrasound pulses reflected from the fetal heart. It consists in a simultaneous recording and analysis of Fetal Heart Rate (FHR) signal, uterine contraction activity and fetal movements. Generally cardiotocograph comprises more number of features. This paper aims to identify the important features, consequently reducing the number of features to assess the fetal heart rate. The features are selected by using Unsupervised Particle Swarm Optimization (PSO) based Relative Reduct and are tested by using various measures of diagnostic accuracy.
Rough Set Based Feature Selection for Egyptian Neonatal Jaundice
Communications in Computer and Information Science, 2014
This paper analyses rough set based feature selection methods for early intervention and preventi... more This paper analyses rough set based feature selection methods for early intervention and prevention of neurological dysfunction and kernicterus that are the major causes of neonatal jaundice. Newborn babies develop some degree of jaundice which requires high medical attention. Improper prediction of diseases may lead to choose unsuitable type of treatment. Traditional rough set based feature selection methods and tolerance rough set based feature selection methods for supervised and unsupervised approach is applied for Egyptian neonatal jaundice dataset. Features responsible for prediction of Egyptian neonatal jaundice is analyzed using supervised quick reduct, supervised entropy based reduct and Unsupervised Tolerance Rough Set based Quick Reduct (U-TRS-QR). Results obtained demonstrate features selected by U-TRS-QR are highly accurate and will be helpful for physicians for early diagnosis.
Unsupervised feature selection using Tolerance Rough Set based Relative Reduct
ABSTRACT
Mining and analysis of clickstream patterns
The explosive growth of the web has drastically changed the way in which information is managed a... more The explosive growth of the web has drastically changed the way in which information is managed and accessed. The large-scale of web data sources and the wide availability of services over the internet have increased the need for effective web data mining techniques and mechanisms . A sophisticated method to organize the layout of the information and assist user navigation
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Papers by Hannah Inbarani