| Autor: |
M. Gunashree, S. Sreedhar Kumar, Syed Thouheed Ahmed, B. Ishwarya, B. Anusha, P. Bhumika |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
New Trends in Computational Vision and Bio-inspired Computing ISBN: 9783030418618 |
| Popis: |
Data Mining is the procedure of extracting information from a data set and transforms information into comprehensible structure for processing. Clustering is data mining technique used to process data elements into their related groups or partition. Thus, the process of partitioning data objects into subclasses is term as ‘cluster’. It consists of data objects with un-unified proposition of high inter similarity and low intra similarity ratios. Thus reflecting the quality of cluster depends on the methods used. Clustering also called data segmentation, divides huge data sets into several groups based upon their similarities. This paper discusses a literature study of various clustering techniques and their comparison on key issues to give guidance for choosing clustering algorithm for a specific research application. The comparison is based on computing performance and clustering accuracy. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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