Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship

Cluster Evaluation of Density Based Subspace Clustering

Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will o...

Model-based subspace clustering

We discuss a model-based approach to identifying clusters of objects based on subsets of attributes, so that the attributes that distinguish a cluster from the rest of the population may depend on the cluster being considered. The method is based on a Pólya urn cluster model for multivariate means and variances, resulting in a multivariate Dirichlet process mixture model. This particular model-...

Constraint-Based Subspace Clustering

In high dimensional data, the general performance of traditional clustering algorithms decreases. This is partly because the similarity criterion used by these algorithms becomes inadequate in high dimensional space. Another reason is that some dimensions are likely to be irrelevant or contain noisy data, thus hiding a possible clustering. To overcome these problems, subspace clustering techniq...

Ranking and Clustering Iranian Provinces Based on COVID-19 Spread: K-Means Cluster Analysis

Introduction: The Coronavirus has crossed geographical borders. This study was performed to rank and cluster Iranian provinces based on coronavirus disease (COVID-19) recorded cases from February 19 to March 22, 2020. Materials and Methods: This cross-sectional study was conducted in 31 provinces of Iran using the daily number of confirmed cases. Cumulative Frequency (CF) and Adjusted CF (ACF)...

Estimating the mixing matrix in Sparse Component Analysis (SCA) based on partial k-dimensional subspace clustering