Performance Analysis of DE over K-Means Proposed Model of Soft Computing

Performance Analysis of DE over K-Means Proposed Model of Soft Computing

Kapil Patidar (Amity School of Engineering and Technology, India), Manoj Kumar (Amity School of Engineering and Technology, India) and Sushil Kumar (Amity School of Engineering and Technology, India)
DOI: 10.4018/978-1-4666-9885-7.ch010
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Abstract

In real world data increased periodically, huge amount of data is called Big data. It is a well-known term used to define the exponential growth of data, both in structured and unstructured format. Data analysis is a method of cleaning, altering, learning valuable statistics, decision making and advising assumption with the help of many algorithm and procedure such as classification and clustering. In this chapter we discuss about big data analysis using soft computing technique and propose how to pair two different approaches like evolutionary algorithm and machine learning approach and try to find better cause.
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Clustering algorithm is broadly classified into four ways partitioning algorithm, hierarchical algorithm, density-based algorithm and grid based algorithm. Partitioning algorithm build a partition of N object into a set of k clusters. The problem of partition clustering has been approached from miscellaneous fields of knowledge, such as artificial neural network, graph theory, expectation maximization algorithms, evolutionary computing, and so on. For the evolutionary computation between them, Differential Evolution (DE) algorithm developed. Is has been shared by many features of classical Genetic Algorithms (GA).

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