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Venugopal, K. R.
- Enhanced Neighborhood Normalized Pointwise Mutual Information Algorithm for Constraint Aware Data Clustering
Abstract Views :192 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, IN
1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, IN
Source
ICTACT Journal on Soft Computing, Vol 6, No 4 (2016), Pagination: 1287-1292Abstract
Clustering of similar data items is an important technique in mining useful patterns. To enhance the performance of Clustering, training or learning is an important task. A constraint learning semi-supervised methodology is proposed which incorporates SVM and Normalized Point wise Mutual Information Computation Strategy to increase the relevance as well as the performance efficiency of clustering. The SVM Classifier is of Hard Margin Type to roughly classify the initial set. A recursive re-clustering approach is proposed for achieving higher degree of relevance in the final clustered set by incorporating ENNPI algorithm. An overall enriched F-Measure value of 94.09% is achieved as compared to existing algorithms.Keywords
Clustering, Constraint Learning, Normalized Pointwise Mutual Information, Recursive Re-Clustering, SVM.
- PTMIBSS:Profiling Top Most Influential Blogger Using Synonym Substitution Approach
Abstract Views :286 |
PDF Views:6
Authors
G. U. Vasanthakumar
1,
R. Priyanka
1,
K. C. Vanitha Raj
1,
S. Bhavani
1,
B. R. Asha Rani
1,
P. Deepa Shenoy
1,
K. R. Venugopal
1
Affiliations
1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, IN
1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 2 (2017), Pagination: 1408-1420Abstract
Users of Online Social Network (OSN) communicate with each other, exchange information and spread rapidly influencing others in the network for taking various decisions. Blog sites allow their users to create and publish thoughts on various topics of their interest in the form of blogs/blog documents, catching the attention and letting readers to perform various activities on them. Based on the content of the blog documents posted by the user, they become popular. In this work, a novel method to profile Top Most Influential Blogger (TMIB) is proposed based on content analysis. Content of blog documents of bloggers under consideration in the blog network are compared and analyzed. Term Frequency and Inverse Document Frequency (TF-IDF) of blog documents under consideration are obtained and their Cosine Similarity score is computed. Synonyms are substituted against those unmatched keywords if the Cosine Similarity score so computed is below the threshold and an improved Cosine Similarity score of those documents under consideration is obtained. Computing the Influence Score after Synonym substitution (ISaS) of those bloggers under conflict, the top most influential blogger is profiled. The simulation results demonstrate that the proposed Profiling Top Most Influential Blogger using Synonym Substitution (PTMIBSS) algorithm is adequately accurate in determining the top most influential blogger at any instant of time considered.Keywords
Blog Document, Content Analysis, Cosine Similarity Score, Influential Blogger, Profiling.References
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