S. Murugan ¹, C. Jothi Venkateswaran ², N. Radhakrishnan ³

Affiliations
1 Department of Computer Science, Nehru Memorial College, Puthanampatti, Tamil Nadu, IN
2 Department of Computer Science, Presidency College, Chennai, Tamil Nadu, IN
3 Geocare Research Foundation, Chennai, Tamil Nadu, IN

Abstract

Detection and extraction of linear features from Remote Sensing Image (RSI) has found many applications as in urban planning, disaster mitigation and environmental monitoring. There were many previous studies in this field appreciating the significance of statistical operators to extract linear features. But in RSI domain, it has a different significance as it involve handling a large data set of multiband data involving complexities in terms of spectral, spatial and temporal domain. Most of the objects in nature were not easily discernable and extracted as they were often contaminated or mixed with other objects and might influence the spectral character of the object. This may be less in urban environment as they exhibit more or less uniform spectral behavior where as in natural setting it may exhibit complex spectral behavior. Present study demonstrates such complexities in extracting linear features in different setting - urban and coastal area - using first order derivative gradient filters.

Keywords

RSI, Spectral, Spatial, Tempora, Linear Future

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R. Vijaya ¹, A. M. Saravanan ², C. Jothi Venkateswaran ³

Affiliations
1 Dept. of Computer Science, Arignar Anna Govt. Arts College, Walajapet - 632 513, Tamil Nadu, IN
2 Dept.of Computer Science, Muthurangam Govt. Arts College, Vellore–632002, Tamil Nadu, IN
3 Dept. of Computer Science, Presidency College, Chennai, Tamil Nadu, IN

Abstract

Identification of objects from heterogeneous regions is one of the challenging tasks in image mining. It can be thought of as partitioning image into clusters based on the image attributes. The purpose of clustering is to identify the similar groupings from a large data set to produce a precise representation of the image. Clustering requires classification of pixels according to some similarity metrics. Classical clustering algorithm, K-Means finds the clusters based on similarity metric. One of the draw back with the standard K-Means algorithm is that it produces accurate results only when applied to images defined by homogeneous region and the cluster are well separated from each other by the way of randomly picking cluster center. In our approach hierarchical clustering algorithm is used to find the initial cluster center and the mass value for the pixels is determined which decides the pixel inclusion into the appropriate clusters. The experimental results show that the new clustering algorithm outperforms well.

Keywords

K-Means Clustering, Hierarchical Clustering, Dempster Shafer Theory

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G. Sudhamathy ¹, C. Jothi Venkateswaran ²

Affiliations
1 Department of Computer Applications, Velammal College of Engineering & Technology, Madurai 625 009, IN
2 Department of Computer Science, Presidency College (Autonomous), Chennai 600 025, IN

Abstract

In this paper, we proposed an efficient approach for frequent pattern mining using web logs - web usage mining and we call this approach as HFPA. In our approach HFPA, the proposed technique is applied to mine association rules from web logs using normal Apriori algorithm, but with few adaptations for improving the interestingness of the rules produced and for applicability for web usage mining. We applied this technique and compared its performance with that of classical Apriori-mined rules. The results indicate that the proposed approach HFPA not only generates far fewer rules than Apriori-based algorithms (FPA), but also generate rules of comparable quality with respect to three objective performance measures namely, Confidence, Lift and Conviction. Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper we have proposed effective pruning techniques that were characterized by the natural web link structures. Our experiments showed that interestingness measures can successfully be used to sort the discovered association rules after the pruning method was applied. Most of the rules that ranked highly according to the interestingness measures proved to be truly valuable to a web site administrator.

Keywords

Web Usage Mining, Web Logs, Association Rules, Interestingness Measures

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P. Umar Sathic Ali ¹, C. Jothi Venkateswaran ²

Affiliations
1 Department of Computer Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Presidency College, Chennai, IN

Abstract

With the rapid growth of online documents available on the World Wide Web necessitate the task of classifying those documents into semantic categories. Text categorization is the task of automatically classifying the textual documents into a set of predefined categories. In this paper, we report the empirical evaluation of lazy learning classifier such as kNN and its variant like distance weighted kNN and our newly proposed evident theoretic kNN for text categorization task over two benchmark datasets. We observed the superiority of evident theoretic kNN method over others in all experiments we conducted.

Keywords

Text Categorization, Lazy Learning, KNN

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