Title : K means algorithm with a covariance matrix compared to partition based clustering algorithms

Type of Material: Thesis
Title: K means algorithm with a covariance matrix compared to partition based clustering algorithms
Researcher: Simhachalam, Boddana
Guide: Hymavathi, T.
Department: Department of Mathematics
Publisher: Adikavi Nannaya University
Place: Rajahmundry
Year: 2018
Language: English
Subject: Mathematics
Mathematical Sciences
Physical and Basic Sciences
Dissertation/Thesis Note: PhD; Department of Mathematics, Adikavi Nannaya University, Rajahmundry; 2018
Fulltext: Shodhganga

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035__|a(IN-AhILN)th_462275
040__|dIN-AhILN|aANUR_533105
041__|aeng
100__|eResearcher|aSimhachalam, Boddana
110__|aDepartment of Mathematics|0U-0005|dRajahmundry|bAdikavi Nannaya University|ein
245__|aK means algorithm with a covariance matrix compared to partition based clustering algorithms
260__|aRajahmundry|c2018|bAdikavi Nannaya University
300__|dDVD
502__|d2018|bPhD|cDepartment of Mathematics, Adikavi Nannaya University, Rajahmundry
518__|d2018|oDate of Award
518__|oDate of Registration|d2011
518__|oDate of Completion|d2018
520__|aData analysis contains several techniques and tools for handling the data. These techniques or algorithms play a notable role to assist the decision makers in making predictions that impact people and enterprises. Clustering or Classification is the core method of data analysis. Clustering is an unsupervised multivariate analysis technique to partition or categorize the dataset into groups (classes or clusters) in a dataset such that the most indiscernible objects belong to the same group while the discernible objects in different groups.
650__|aMathematical Sciences|2UGC
650__|aPhysical and Basic Sciences|2AIU
653__|aMathematics
700__|aHymavathi, T.|eGuide
856__|uhttp://shodhganga.inflibnet.ac.in/handle/10603/314817|yShodhganga
905__|afromsg

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