K means algorithm with a covariance matrix compared to partition based clustering algorithms
| dc.contributor.guide | Hymavathi, T. | |
| dc.coverage.spatial | ||
| dc.creator.researcher | Simhachalam, Boddana | |
| dc.date.accessioned | 2021-02-11T11:13:30Z | |
| dc.date.available | 2021-02-11T11:13:30Z | |
| dc.date.awarded | 2018 | |
| dc.date.completed | 2018 | |
| dc.date.registered | 2011 | |
| dc.description.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 | |
| dc.description.note | ||
| dc.format.accompanyingmaterial | DVD | |
| dc.format.dimensions | ||
| dc.format.extent | ||
| dc.identifier.uri | http://hdl.handle.net/10603/314817 | |
| dc.language | English | |
| dc.publisher.institution | Department of Mathematics | |
| dc.publisher.place | Rajahmundry | |
| dc.publisher.university | Adikavi Nannaya University, Rajahmundry | |
| dc.relation | ||
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Mathematics | |
| dc.subject.keyword | Mathematics Applied | |
| dc.subject.keyword | Physical Sciences | |
| dc.title | K means algorithm with a covariance matrix compared to partition based clustering algorithms | |
| dc.title.alternative | ||
| dc.type.degree | Ph.D. |
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