Determining the number of clusters in a data set

Consensus clustering

Imputation (statistics)

Missing data

Correlation clustering

Canopy clustering algorithm

Algorithm

Data mining

Machine learning

Artificial intelligence

Fuzzy clustering

Data stream clustering

Single-linkage clustering

Computer science

Cluster analysis

CURE data clustering algorithm

Big Data Mining and Analytics

Density-based clustering is an important category among clustering algorithms. In real applications, many datasets suffer from incompleteness. Traditional imputation technologies or other techniques for handling missing values are not suitable for density-based clustering and decrease clustering result quality. To avoid these problems, we develop a novel density-based clustering approach for incomplete data based on Bayesian theory, which conducts imputation and clustering concurrently and makes use of intermediate clustering results. To avoid the impact of low-density areas inside non-convex clusters, we introduce a local imputation clustering algorithm, which aims to impute points to high-density local areas. The performances of the proposed algorithms are evaluated using ten synthetic datasets and five real-world datasets with induced missing values. The experimental results show the effectiveness of the proposed algorithms.

Effective density-based clustering algorithms for incomplete data