Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering

Minkowski Metric , Feature Weighting and Anomalous Cluster Initializing in K - Means Clustering Renato

This paper represents another step in overcoming a drawback of K-Means, its lack of defense against noisy features, by using feature weights in the criterion. The Weighted K-Means method by Huang et al. is extended to the corresponding Minkowski metric for measuring distances. Under Minkowski metric the feature weights become intuitively appealing feature rescaling factors in a conventional K-M...

Learning feature weights for K-Means clustering using the Minkowski metric

A-Wardpβ: Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation

In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently large number of clusters. This allows the cluster merging process to start f...

Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

We consider the weighted K-Means algorithm with distributed centroids aimed at clustering data sets with numerical, categorical and mixed types of data. Our approach allows given features (i.e., variables) to have different weights at different clusters. Thus, it supports the intuitive idea that features may have different degrees of relevance at different clusters. We use the Minkowski metric ...

Entropy Reduction Based On K-Means Clustering And Neural Network/SVM Classifier

Clustering is the unsupervised learning problem. Better Clustering improves accuracy of search results and helps to reduce the retrieval time. Clustering dispersion known as entropy which is the disorderness that occur after retrieving search result. It can be reduced by combining clustering algorithm with the classifier. Clustering with weighted k-mean results in unlabelled data. This paper pr...