| Type of Material: | Thesis |
| Title: | Celestial Clustering An Accurate and Efficient Unsupervised Learning Method on Large High Dimensional Data |
| Researcher: | Shibu Kumar, K B |
| Guide: | Samuel, Philip |
| Department: | Department of Information Technology |
| Publisher: | Cochin University of Science & Technology, Cochin |
| Place: | Cochin |
| Year: | 2023 |
| Language: | English |
| Subject: | Celestial Clustering | Centroid Based Celestial Clustering | Computer Science | Consignment Delivery System | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Information Technology, Cochin University of Science & Technology, Cochin, Cochin; 2023 |
| Fulltext: | Shodhganga |
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| 001 | 456265 | |
| 003 | IN-AhILN | |
| 005 | 2024-10-08 17:29:13 | |
| 008 | __ | 241008t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_456265 |
| 040 | __ | |aCUST_682022|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aShibu Kumar, K B|eResearcher |
| 110 | __ | |aDepartment of Information Technology|bCochin University of Science & Technology, Cochin|dCochin|ein|0U-0253 |
| 245 | __ | |aCelestial Clustering An Accurate and Efficient Unsupervised Learning Method on Large High Dimensional Data |
| 260 | __ | |aCochin|bCochin University of Science & Technology, Cochin|c2023 |
| 300 | __ | |axxi, 216|dDVD |
| 502 | __ | |cDepartment of Information Technology, Cochin University of Science & Technology, Cochin, Cochin|d2023|bPhD |
| 518 | __ | |d2024|oDate of Award |
| 518 | __ | |oDate of Registration|d2018 |
| 520 | __ | |aOut of the different unsupervised learning methods, clustering is the most prominent and widely used mechanism in pattern analysis of unlabelled data. Clustering is widely used in various fields such as market segmentation, gene expression analysis, natural language processing and image processing. Though there have been a number of clustering mechanisms for various domains, the choice of algorithm and the evaluation of results depend on the nature of the data and the specific application. However, many clustering mechanisms are sensitive to initial conditions and can lead to local optima, yielding incorrect results. Also, a number of them lack the capability to handle large, noisy and high dimensional data and very often find it difficult to balance between accuracy and efficiency. This research focuses on addressing the gaps in the current solutions in an efficient and effective manner by proposing a celestial clustering approach. It proposes a novel general partitioning based clustering method with a lin |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aCelestial Clustering |
| 653 | __ | |aCentroid Based Celestial Clustering |
| 653 | __ | |aComputer Science |
| 653 | __ | |aConsignment Delivery System |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |eGuide|aSamuel, Philip |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/571374|yShodhganga |
| 905 | __ | |afromsg |
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