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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Meta Similarity Noise-free Clusters Using Dynamic Minimum Spanning Tree with Self-Detection of Best Number of Clusters

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Author T. Karthikeyan, S. John Peter
ISSN 2079-8407
On Pages 192-200
Volume No. 2
Issue No. 4
Issue Date April 01, 2011
Publishing Date April 01, 2011
Keywords Euclidean minimum spanning tree, Subtree, Clustering, Eccentricity, Center, Hierarchical clustering, Dendrogram, Cluster validity, Cluster Separation, Outliers


Abstract

Clustering is a process of discovering group of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input parameters. Minimum Spanning Tree clustering algorithm is capable of detecting clusters with irregular boundaries. Detecting outlier in database (as unusual objects) is a big desire. In data mining detection of anomalous pattern in data is more interesting than detecting inliers. In this paper we propose a Minimum Spanning Tree based clustering algorithm for noise-free or pure clusters. The algorithm constructs hierarchy from top to bottom. At each hierarchical level, it optimizes the number of cluster, from which the proper hierarchical structure of underlying data set can be found. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of clusters at each level. The algorithm works in two phases. The first phase of the algorithm create clusters with guaranteed intra-cluster similarity, where as the second phase of the algorithm create dendrogram using the clusters as objects with guaranteed inter-cluster similarity. The first phase of the algorithm uses divisive approach, where as the second phase uses agglomerative approach. In this paper we used both the approaches in the algorithm to find Best number of Meta similarity clusters.  

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