tailieunhanh - A review on various clustering techniques in data mining

Clustering is an unsupervised learning problem which is used to determine the intrinsic grouping in a set of unlabeled data [3]. The grouping of objects is done on the principle of maximizing the intra-cluster similarity and minimizing the inter-cluster similarity in such a way that the objects in the same group/cluster share some similar properties/traits [4]. | ISSN:2249-5789 Mamta Mor, International Journal of Computer Science & Communication Networks,Vol 6(3),138-142 A Review on Various Clustering Techniques in Data Mining Mamta Mor mamtamor12121990@ Abstract This paper presents a review on various clustering techniques used in data mining. Data mining is the task of retrieving useful and hidden knowledge from data sets [1] [2]. Clustering is one of the important tasks of data mining. Clustering is an unsupervised learning problem which is used to determine the intrinsic grouping in a set of unlabeled data [3]. The grouping of objects is done on the principle of maximizing the intra-cluster similarity and minimizing the inter-cluster similarity in such a way that the objects in the same group/cluster share some similar properties/traits [4]. pair of objects. On the basis of similarity or dissimilarity clustering can be classified into two types: a) Distance based clustering b) Conceptual clustering In distance based clustering the objects/instances are put into clusters on the basis of distance criteria: two or more objects belong to the same cluster if they are “closer” to the centroid of that particular cluster. The basic idea behind distance based clustering is to minimize the intra-cluster distance and maximize the inter-cluster distance, which is shown in Figure 1. 1. Introduction Clustering techniques are useful in various applications of real world including data/text mining, voice mining, image processing, web mining etc. It is a main task of exploratory data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics [3] [5]. Clustering is the technique of partitioning the data being mined into several clusters of data objects, in such a way that: a) The objects in a cluster resemble to each other to a great extent; and b) The objects of a cluster are much different .

• Cơ sở dữ liệu
• International journal of computer science & communication networks
• Various clustering techniques in data mining
• Various clustering technique
• The inter cluster similarity
• Minimizing the inter cluster similarity