An Enhanced Fuzzy Based Linkage Clustering Algorithm (EFCA) in High Dimensional Data
DOI:
https://doi.org/10.26438/ijcse/v8i2.1217Keywords:
Data mining,, Big data cluster analysi, Fuzzy, LinkageAbstract
In data mining, clustering algorithm is a powerful meta-learning tool to precisely examine the huge volume of data created by recent applications. In particular, their major objective is to group data into clusters such that data points are grouped in the similar cluster when they are “similar” according to specific metrics. Several clustering algorithms have been developed to deal with very large number of features or with a very high number of dimensions, but they are often not practical when the data is large in both aspects. To address these issues, this paper work, developed an Enhanced Fuzzy based Linkage Clustering Algorithm (EFCA), which combines FCM and cluster assignment strategy to solve the optimization problem during high dimensional data processing. The proposed EFCA approach it can work with large volumes of high dimensional dataset for discovering the outliers. The experimental results shown that the proposed EFCA performance to improve 21.9% especial in terms of Partition Accuracy (PA), Dunn Index (DI) improves 28 %, and Computational time improves 16.4% compared with other existing clusiVAT and FensiVAT algorithms.
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