Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/314817
Title: | K means algorithm with a covariance matrix compared to partition based clustering algorithms |
Researcher: | Simhachalam, Boddana |
Guide(s): | Hymavathi, T. |
Keywords: | Mathematics Mathematics Applied Physical Sciences |
University: | Adikavi Nannaya University, Rajahmundry |
Completed Date: | 2018 |
Abstract: | Data analysis contains several techniques and tools for handling the data. These newlinetechniques or algorithms play a notable role to assist the decision makers in making newlinepredictions that impact people and enterprises. Clustering or Classification is the core newlinemethod of data analysis. Clustering is an unsupervised multivariate analysis technique to newlinepartition or categorize the dataset into groups (classes or clusters) in a dataset such that the newlinemost indiscernible objects belong to the same group while the discernible objects in different newlinegroups. newline |
Pagination: | |
URI: | http://hdl.handle.net/10603/314817 |
Appears in Departments: | Department of Mathematics |
Files in This Item:
File | Description | Size | Format | |
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80_recommendation.pdf | Attached File | 62.13 kB | Adobe PDF | View/Open |
certificate-bsm.pdf | 100.99 kB | Adobe PDF | View/Open | |
chapter-i-bsm.pdf | 429 kB | Adobe PDF | View/Open | |
chapter-ii-bsm.pdf | 233.02 kB | Adobe PDF | View/Open | |
chapter-iii-bsm.pdf | 753.4 kB | Adobe PDF | View/Open | |
chapter-iv-bsm.pdf | 689.5 kB | Adobe PDF | View/Open | |
chapter-v-bsm.pdf | 820.81 kB | Adobe PDF | View/Open | |
chapter-vi-bsm.pdf | 1.18 MB | Adobe PDF | View/Open | |
chapter-vii-bsm.pdf | 798.55 kB | Adobe PDF | View/Open | |
preliminary pages-bsm.pdf | 113.47 kB | Adobe PDF | View/Open | |
title-bsm.pdf | 6.41 MB | Adobe PDF | View/Open |
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