Comparative Weka Analysis of Clustering Algorithm's

Comparative Weka Analysis of Clustering Algorithm's

Автор: Harjot Kaur, Prince Verma

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 8, 2017 года.

Бесплатный доступ

Data mining is a procedure of mining or obtaining a pertinent volume of data or information making the data available for understanding and processing. Data analysis is a common method across various areas like computer science, biology, telecommunication industry and retail industry. Data mining encompass various algorithms viz. association rule mining, classification algorithm, clustering algorithms. This survey concentrates on clustering algorithms and their comparison using WEKA tool. Clustering is the splitting of a large dataset into clusters or groups following two criteria ie. High intra-class similarity and low inter-class similarity. Every cluster or group must contain one data item and every data item must be in one cluster. Clustering is an unsupervised technique that is fairly applicable on large datasets with a large number of attributes. It is a data modelling technique that gives a concise view of data. This survey tends to explain all the clustering algorithms and their variant analysis using WEKA tool on various datasets.

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Data Mining, Clustering, Partitioning Algorithm, Hierarchical Clustering Algorithm, CURE, CHAMELEON, BIRCH, Density Based Clustering Algorithm, DENCLUE, OPTICS, WEKA Tool

Короткий адрес: https://sciup.org/15012674

IDR: 15012674

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