The k-histogram algorithm extends the k-means algorithm to categorical domain by replacing the means of clusters with histograms, and dynamically updates histograms in the clustering process.
Sep 13, 2005 · In this paper, we present k-histogram, a new efficient algorithm for clustering categorical data. The k-histogram algorithm extends the k-means ...
In this paper, we present k-histogram, a new efficient algorithm for clustering categorical data. The k-histogram algorithm extends the k-means algorithm to ...
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In this paper, a new strategy called Clustering Algorithm Based on Histogram Threshold (HTCA) is proposed to improve the execution time. The HTCA method ...
We present two new clustering algorithms called k-sets and k-swaps for data where each object is a set. First, we define the mean of the sets in a cluster, ...
Squeezer is an effective histogram based approach for categorical data stream clustering. Drawback of Squeezer is that it is not scalable in terms of memory ...
K-Histograms An Efficient Clustering Algorithm for Categorical Dataset-Clustering categorical data is an integral part of data mining and has attracted much ...
In 1998, Huang [20] proposed the k-modes algorithm for categorical data clustering. ... In k-histograms, each cluster is captured by a histogram of the ...
Apr 19, 2021 · The KMeans algorithm attempts to cluster data by creating groups that minimise the within-cluster sum-of-squared differences, aka inertia.
K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset ... 2005. TLDR. Experimental results on real datasets show that k-histogram algorithm ...