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Articles by M. Punithavalli
Total Records ( 4 ) for M. Punithavalli
  S.C. Punitha , V. Thavavel and M. Punithavalli
  Data mining is a process of analyzing data from different perspectives and summarizing it into valuable information. It consist of two activities such as clustering and classification. It mainly works with numeric data, text data and the web data. Text-based algorithms have problems when dealing with different languages (synonyms, homonyms). Also, web pages contain other forms of information except text, such as images or multimedia. As a consequence, hybrid document clustering approaches have been proposed in order to combine the advantages and limit the disadvantages of the existing approaches. The main motivation behind ontology is that different people have different needs with regard to the clustering of texts. The hybrid schemes are developed using ontology and the frequent item clustering of various algorithms Ontology Based Apriori Based Clustering, Ontology based FP-Growth Based Clustering, Ontology based FP-Bonsai Clustering Algorithm have been proposed to resolve the disadvantages of existing approaches. The performance of this enhanced document clustering algorithm was tested vigorously using different datasets with performance measures to show the efficiency in clustering. Hence Ontology based FP-Bonsai Clustering Algorithm (OFPBC) shows significant improvement in terms of purity of clustering. The result shows that the datasets namely Reuters 21578,20 new Group and TDT2 which results the accuracy 0.840, 0.817 and 0.847 in OFPBC, respectively.
  Mrs. P. Sumathi and M. Punithavalli
  This study provides a design framework for the adoption of grid computing for e-governance applications. Problem statement: E-Governance is the application of information and communication technology to achieve efficiency, effectiveness, transparency and accountability in Government to Government (G2G), Government to Employee (G2E), Government to Citizen (G2C) and Government to Business (G2B). It enables citizens to make best use of automated administration processes that are accessible on-line. Grid computing is an ideal solution to this type of applications and the study presents how grid computing can be used to effectively and efficiently handle such huge data. In this study, we illustrate the creation of a virtual environment by using existing Grid technologies to specific e-governance applications on distributed resources. Approach: A Grid generally refers to an infrastructure that involves the integrated and collaborative use of all computing resources into a single virtual computing environment. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. Grid computing is an ideal solution to this type of applications and the study presents how grid computing can be used to effectively and efficiently handle such huge data. Results: The applications were run with the grid environment and without Grid environment. The results obtained were compared with the time and the number of jobs. The obtained results using grid environment were more significant and promising. Conclusions: Implementing an E-Governance solution will lower the cost of developing, deploying, managing government solutions and providing better services to citizens.
  E. Ramaraj and M. Punithavalli
  The biological implications of bioinformatics can already be seen in various implementations. Biological taxonomy may seem like a simple science in which the biologists merely observe similarities among organisms and construct classifications according to those similarities[1], but it is not so simple. By applying data mining techniques on gene sequence database we can cluster the data to find interesting similarities in the gene expression data. One of the applications of such kind of clustering is taxonomically clustering the organisms based on their gene sequential expressions. In this study we outlined a method for taxonomical clustering of species of the organisms based on the genetic profile using Principal Component Analysis and Self Organizing Neural Networks. We have implemented the idea using Matlab and tried to cluster the gene sequences taken from PAUP version of the ML5/ML6 database. The taxa used for some of the basidiomycetous fungi form the database. To study the scalability issues another large gene sequence database was used. The proposed method clustered the species of organisms correctly in almost all the cases. The obtained were more significant and promising. The proposed method clustered the species of organisms correctly in almost all the cases. The obtained results were more significant and promising.
  E. Vijayakumar and M. Punithavalli
  Graphical User Interfaces (GUI) are important components of Event-Driven software that are used mainly for improving user-computer interactions. As the number of graphical controls that the user can select using mouse or key board is very high, the number of test cases generated is also very high. Thus, the test cases generation process has to be optimized. This research performs this in three steps by enhancing the three operations, namely; test case generation, reduction and prioritization. Experimental results prove that the methods proposed have optimized the process of test case generation and has improved the accuracy of error detection rate. A maximum of 99.25% fault detection rate was obtained which shows that the proposed amalgamation of techniques are successful and can be used by the 21st century software.
 
 
 
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