Informatics Publishing Limited

Ananthi Sheshasayee ¹, P. Sharmila ²

Affiliations
1 Department of Computer Science and Applications, Quaid-e-Milleth government college for Women, Chennai, Tamil Nadu, IN
2 Department of Computer Applications, Vivekanandha Institute of Information and Management Studies, Tiruchengode, Tamil Nadu, IN

Abstract

The Requirement Engineering is the most important phase of the software development life cycle which is used to translate the imprecise, incomplete needs and wishes of the potential users of software into complete, precise and formal specifications. These specifications can be decomposed on application of a data mining techniques, clustering. The process of clustering the requirements allows reducing the cost of software development and maintenance. In this research two most frequently used algorithms in clustering namely k means and fuzzy c means are used. The output generated is then analyzed for evaluating the performance of the two clustering algorithms. The requirements specified by the different stakeholders of the library are used as the input. The data mining tool WEKA was used for clustering. The clustering algorithms were then analyzed for accuracy and performance. On analysis the fuzzy c means algorithm was found to be more suitable for clustering of library requirements. The results proved to be satisfactory.

Keywords

Clustering, Data Mining, Fuzzy C Means, K Means, Library, Requirements

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Ananthi Sheshasayee ¹, J. V. N. Lakshmi ²

Affiliations
1 PG and Department of Computer Science and Research, Quaid – E – Millath Government. College for Women, Chennai, - 600002, Tamil Nadu, IN
2 Department of Research in Computer Science, SCSVMV University, Kanchipuram - 631561, Tamil Nadu, IN

Abstract

Background: As massive data acquisition and storage becomes increasingly affordable, a wide variety of enterprises are engaged in sophisticated data analysis. The amount of digital information which is majorly unstructured produced is exceeding day by day. Method: MapReduce programming method is easily applicable to many different learning algorithms. Machine Learning is at the core of data analysis. Traditional machine learning algorithms speed up at a time to fit the statistical query model on multicore computers. Data Sharing is avoided by Hadoop whereas Machine learning Algorithm needs data to be stored in single place. The method compares the machine learning algorithms on MapReduce paradigm for evaluating speed. Findings: MapReduce programming model enables easy development of scalable parallel applications to process large clusters of data. Hadoop Distributed File System runs the MapReduce jobs which influence the performance significantly while handling huge data set stored on different nodes of a multi node cluster. This paper analyses on developing machine learning algorithms on Hadoop to process large clusters of data. Analyzing logistic regression algorithms on MapReduce for evaluating the performance to speed up processing by developing a cost model. The attributes of the system are evaluated for improving time efficiency. The objective is to provide ad hoc performance for MapReduce programs which run on large data sets. Improvements: A method for optimizing job assignment on machine learning is implemented in order to minimize the total execution time. This feature improves the productivity of MapReduce users to optimize performance efficiency.

Keywords

Big Data, HADOOP, HDFS, Machine Learning, Map Reduction

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