Subspace Clustering Algorithm Based on Multi-rule Constraint

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...

Clustering Based Multi-Objective Rule Mining using Genetic Algorithm

Multi-Objective Genetic Algorithm (MOGA) is a new approach for association rule mining in the market-basket type databases. Finding the frequent itemsets is the most resource-consuming phase in association rule mining, and always does some extra comparisons against the whole database. This paper proposes a new algorithm, Cluster-Based MultiObjective Genetic Algorithm (CBMOGA) which optimizes th...

Subspace Selective Ensemble Algorithm Based on Feature Clustering

A feature-clustering-based subspace selective ensemble learning algorithm was proposed to improve ensemble classifier performance, allowing for high dimensional data sets. First, features were clustered on weighted average linkage method and reduced subspaces were generated by extracting an attribute from each feature cluster. Then the feature reduced subsets served as inputs of individual GA-S...

Constraint Based Subspace Clustering for High Dimensional Uncertain Data

CLUS: Parallel Subspace Clustering Algorithm on Spark

Subspace clustering techniques were proposed to discover hidden clusters that only exist in certain subsets of the full feature spaces. However, the time complexity of such algorithms is at most exponential with respect to the dimensionality of the dataset. In addition, datasets are generally too large to fit in a single machine under the current big data scenarios. The extremely high computati...