A Patent Technique of Jaccard Discrete (J-DIS) Similarity Clustering Algorithm

Imtiaz, Sharjeel and Saadiah, Yahya (2014) A Patent Technique of Jaccard Discrete (J-DIS) Similarity Clustering Algorithm. Proceedings of the 2nd International Conference on Applied Information and Communications Technology (ICAICT 28-29 April 2014).

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Abstract

Traditionally, the classification object yields homogeneous object to separate cluster. Few authors investigated clustering based on k-Means to distinguish intrusions based on the particular class. Mostly, k-Means algorithm finds out similarity between the object based on distance vector for smallest dataset. We proposed a new approach Jaccard Discrete (J-DIS) based approach which is combines with k-Means to find most similar measures over features attribute values in a larger dataset. Further, this paper is describing best suitable larger dataset taken from KDD CUP-99 dataset [1].Moreover, the J-DIS k-Means approach can be applied over clinical informatics and wireless clustering based routing protocols.

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