Avoiding Objects with few Neighbors in the KMeans Process and Adding ROCK Links to Its Distance
Copyright policy )Avoiding Objects with few Neighbors in the KMeans Process and Adding ROCK Links to Its Distance
K-means is considered as one of the most common and powerful algorithms in data clustering, in this paper we're going to present new techniques to solve two problems in the K-means traditional clustering algorithm, the 1 problem is its sensitivity for outliers, in this part we are going to depend on a function that will help us to decide if this object is an outlier or not, if it was an outlier it will be expelled from our calculations, that will help the K-means to make good results even if we added more outlier points; in the second part we are going to make K-means depend on Rock links in addition to its traditional distance, Rock links takes into account the number of common neighbors between two objects, that will make the K-means able to detect shapes that can't be detected by the traditional K-means. General Terms Data Clustering Algorithms, K-means, ROCK, Centroids' Initialization.
- Islamic University Bangladesh
- Islamic University of Gaza Palestinian-administered areas
k-means, rock, electing centroids, initializing k-means, centroids' initialization robust k-means, rock links, optimized k-means, optimizing k-means distance measurement, general terms data clustering algorithms
k-means, rock, electing centroids, initializing k-means, centroids' initialization robust k-means, rock links, optimized k-means, optimizing k-means distance measurement, general terms data clustering algorithms
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