Informatics Publishing Limited

S. Gayathri ¹, M. Mary Metilda ², S. Sanjai Babu ¹

Affiliations
1 Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 Queen Mary's College, Chennai - 600004, Tamil Nadu, IN

Abstract

Background/Objective: A high dimensional data is a dataset that ranges from a few to a hundreds of dimensions. Clustering such datasets needs an efficient algorithm such as Proclus but the algorithm has a drawback of ignoring cluster with small data points. So the proposed paper gives an ensemble of clustering that combines technique of two clustering algorithms to achieve a quality cluster of even small data points. Methods/Statistical Analysis: The research paper adapts a novel method of implementing a density based approach over a Proclus algorithm to cluster even small data points. These combined algorithms are tested using synthetic datasets. The Proclus algorithm is modified at a specific point where the density based algorithm is implemented. Findings: The results of the proposed algorithm are found to contain more clusters than mere Proclus algorithm does. The results is as such because in Proclus clustering the data point whose size is small are ignored so that only clusters with large number of data points can exists. However after the involvement of the shared nearest neighbor density based algorithm even the small data points are clustered which paves way for a more accurate and an efficient clustering process especially in a high dimensional data. Applications/Improvements: The application is a combination of two efficient algorithms but implemented in a simple way thereby reducing the complexity of the algorithm. The proposed technique can be applied on all high dimensional datasets irrespective of their sizes and shapes.

Keywords

Density based Approach, High Dimensional Data, Proclus, SNN Algorithm.

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S. Gayathri ¹, M. Mary Metilda ², S. Sanjai Babu ¹

Affiliations
1 Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 Queen Marys College, Chennai - 600004, Tamil Nadu, IN

Abstract

Background/Objectives: Clustering and Sequential Pattern Mining is two most important unsupervised learning algorithms. The objective is to mine small projected databases rejected by Frequent Pattern - Projected Sequential Pattern mining (FreeSpan) technique using a weighted distance metric clustering method, a process of finding the distance between the small data points and cluster it so that it cannot be rejected. Methods/Statistical Analysis: The method involves the implementation of a distance metric clustering algorithm over a FreeSpan technique to cluster the data points of small projected databases. The FreeSpan technique can be considered as an ensemble of clustering and sequential pattern mining methods. Findings: The clustering method clusters the data points resulted from the FreeSpan technique that are ignored after the scanning process as their sizes are very small. The clustered data therefore gathers the ignored data points thereby providing an accurate clustered data containing small data points which results is trustable sequential pattern for future predictions. The proposed system reduces the complexity by incorporating just a single clustering algorithm. Therefore the major operations of the algorithm remain undisturbed and give its efficient output and also the output is found to be accurate and stable. Applications/Improvements: The technique proposed in the paper can be applied to datasets that needs to be clustered for decision making. The same technique holds good and can be made applicable to high dimensional views.

Keywords

Clustering, Distance Metric Method, FreeSpan, Projected Databases.

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