A Novel Multi View Clustering Technique for Group the Data Objects in Process Mining
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2017 by IJETT Journal | ||
| Volume-44 Number-1 |
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| Year of Publication : 2017 | ||
| Authors : Panila Lokanadham, Jayanthi Rao Madina |
Citation
Panila Lokanadham, Jayanthi Rao Madina "A Novel Multi View Clustering Technique for Group the Data Objects in Process Mining", International Journal of Engineering Trends and Technology (IJETT), V44(1),24-27 February 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Clustering is the process of grouping objects based on some notion of similarity. It is commonly applied for exploratory analysis, segmentation, pre-processing and data summarization. Similarity is dependent on the features describing data. Clustering ensembles are a common approach to clustering problem, which combine a collection of clustering into a superior solution. The key issues are how to generate different candidate solutions and how to combine them. Common approach for generating candidate clustering solutions ignores the multiple representations of the data and the standard approach of simply selecting the best solution from candidate clustering solutions ignores the fact that there may be a set of clusters from different candidate clustering solutions which can form a better clustering solution. Multi view clustering can be applied at various stages of the clustering paradigm. This paper proposes a novel multi-view clustering algorithm that combines different ensemble techniques via various similarity metrics have been used to measure the similarity between data objects. In the novel multi view clustering algorithm contains mainly two techniques; the first technique is used to generate multiple partitions from each of the single view of a multi-view dataset. After completion of multi view of data set we can perform the clusterization process on multi view data set. In this paper we are implementing clustering process we are using K Means algorithm. After completion of clustering process take those clusters and combine those clusters will get an efficient cluster groups. By performing combining the clusters groups we are using cluster based similarity matrix. By implementing those concepts we can improve efficiency for performing the clustering process and also the cluster groups will contains most relevant datasets.
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Keywords
data mining, cluster, cluster based similarity matrix, process mining, dataset, data objects.