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Dissimilarity Computation for Objects of Different Variable Types

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dc.contributor.author Han, Zin Nyein Nyein
dc.contributor.author Tun, Ei Ei Moe
dc.date.accessioned 2019-07-29T07:25:25Z
dc.date.available 2019-07-29T07:25:25Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1459
dc.description.abstract Clustering is the process of grouping a set of physical or abstract objects into classes of similar objects is called clustering. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the object in other cluster. Measuring the dissimilarity between data objects is one of the primary tasks for distance-based techniques in data mining and machine learning, e.g., distance-based clustering and distance-based classification. The quality of clustering can be accessed based on dissimilarity measures of objects which can be computed for various types of data. In this paper, we propose general framework for measuring a dissimilarity betweens various data analysis is proposed. The key idea is to consider the dissimilarity between two values of an attribute as a combination of dissimilarities between the conditional probability distributions of other attributes given these two values. In this system, the similarity is guessed by computing the dissimilarity measure between two objects. This can get the most similar values and the least similar values before clustering analysis. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.subject dissimilarity measure en_US
dc.subject cluster analysis en_US
dc.subject mixes types objects. en_US
dc.title Dissimilarity Computation for Objects of Different Variable Types en_US
dc.type Article en_US


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