S. Prabakaran

A WAVELET APPROACH FOR CLASSIFICATION OF MICROARRAY DATA

Microarray technologies facilitate the generation of vast amount of bio-signal or genomic signal data. The major challenge in processing these signals is the extraction of the global characteristics of the data due to their huge dimension and the complex relationship among various genes. Statistical methods are used in broad spectrum in this domain. But, various limitations like extensive preprocessing, noise sensitiveness, requirement of critical input parameters and prior knowledge about the microarray dataset emphasise the need for better exploratory techniques. Transform oriented signal processing techniques are successful in many data processing techniques like image and video processing. But, the use of wavelets in analyzing the microarray bio-signals is not sufficiently probed. The aim of this paper is to propose a wavelet power spectrum based technique for dimensionality reduction through gene selection and classification problem of gene microarray data. The proposed method was administered on such datasets and the results are encouraging. The present method is robust to noise since no preprocessing has been applied. Also, it does not require any critical input parameters or any prior knowledge about the data which is required in many existing methods.

Classification of multi class dataset using wavelet power spectrum

Data mining techniques are widely used in many fields. One of the applications of data mining in the field of the Bioinformatics is classification of tissue samples. In the present work, a wavelet power spectrum based approach has been presented for feature selection and successful classification of the multi class dataset. The proposed method was applied on SRBCT and the breast cancer datasets which are multi class cancer datasets. The selected features are almost those selected in previous works. The method was able to produce almost 100% accurate classification results. The method is very simple and robust to noise. No extensive preprocessing is required. The classification was performed with comparatively very lesser number of features than those used in the original works. No information is lost due to the initial pruning of the data usually performed using a threshold in other methods. The method utilizes the inherent nature of the data in performing various tasks. So, the method can be used for a wide range of data.

Cluster optimization for improved web usage mining using ant nestmate approach

The web usage mining uses data mining techniques to discover interesting usage patterns from web data. Web personalization uses web usage mining techniques for the process of customization. Customization involves knowledge acquisition done by analysis of users navigational behavior. A user when goes online would like to get the links which suits his requirements or usage in the website he visits. The next business requirement in the online industry will be personalizing/customizing the web page fulfilling for each individuals requirement. The personalization of the web page will involve clustering of different web pages having common usage pattern. As the size of the cluster goes on increasing due to increase in users or growth of interest of users it will become inevitable need to optimize the clusters. This paper proposes a cluster optimizing methodology based on ants nestmate recognition ability and is used for eliminating the data redundancies that may occur after the clustering done by the web usage mining methods. For clustering an ART1-neural network based approach is used. “AntNestmate approach for cluster optimization” is presented to personalize web page clusters of target users.

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.