Author : C.Vinothini 1
Date of Publication :30th March 2018
Abstract: Data mining involves the association rule learning, classification, summarization, regression, anomaly detection and clustering. Clustering is a data mining technique to group the related data into a cluster and unrelated data into different clusters. Based on the recently described cluster models, there are a lot of clustering that can be applied to a data set in order to partitionate the information. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is the most well-known density-based clustering algorithm. The aim is to identify dense regions, which can be measured by the number of objects nearest to a given point. Unlike K-Means, DBSCAN does not require the number of clusters as a parameter. It infers the number of clusters on its data, and it can detect clusters of arbitrary shape. Density-based clustering algorithms try to find clusters based on the density of data points in a region. For the experimental work, we have used the milk data set. The results were analyzed and practically tested under MATLAB tools
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