PriTri: An Innovative Algorithm for Clustering Categorical Data in Data Warehouse

An improved opposition-based Crow Search Algorithm for Data Clustering

Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

SCLOPE: An Algorithm for Clustering Data Streams of Categorical Attributes

Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose SCLOPE, a novel algorithm based on CLOPE’s intuitive observation about cluster histograms. Unlike CLOPE however, our algorithm is very fast and operates within the constraints of a data stream environment. In particular, we designed SCLOPE a...

An Improved K-means Algorithm for Clustering Categorical Data

Most of the earlier work on clustering is mainly focused on numerical data the inherent geometric properties of which can be exploited to naturally define distance functions between the data points. However, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The k-means algorithm is well known for its efficiency in this respect. At the...

An iterative initial-points refinement algorithm for categorical data clustering