Abstract
The main purpose of the process of data mining is to extract useful information from a huge amount of dataset. As one of the most important tasks in data mining, clustering is the process of grouping object attributes and features such that the data objects in one group are more similar than data objects in another group. It is a form of unsupervised learning that means how data should be grouped the data objects (similar types) together will be not known in advance. The algorithms used for clustering are k-means algorithm, k-medoid algorithm, k-nearest neighbour algorithm, k-mode algorithm etc. The K-Mode Algorithm is an eminent algorithm which is an extension of the K-Means Algorithm for clustering data set with categorical attributes and is famous for its simplicity and speed. The ‘Simple Matching Dissimilarity’ measure is used instead of Euclidean distance and the ‘Mode’ of clusters is used instead of ‘Means’. In this paper, review on the K-Mode Algorithm is done.
Highlights
Data mining may be defined as the task of process the data from different dimensions and in turn summarized it into the useful information
K-Means Algorithm is a partitioning based algorithm for clustering that creates clusters of the same type of data according to their closeness to each other based on the Euclidean distance [5]
The determination of grouping in a set of unlabelled information on the basis of its features is the main objective of clustering
Summary
Data mining may be defined as the task of process the data from different dimensions and in turn summarized it into the useful information. The data mining operations and algorithms are required to deal with different types of attributes. In this sophisticated data analysis tools are used along with visualization techniques to segment the data. A. Supervised learning: In this learning, data includes together the input and the desired result. Supervised learning: In this learning, data includes together the input and the desired result It is the fast and a perfect learning method. Unsupervised learning: The desired result is not provided to the unsupervised model during learning procedure This method can be used to cluster the input data in classes on the basis of their statistical properties
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