Clustering algorithms aim at finding dense regions of data based on similarities and
dissimilarities of data points. Noise and outliers contribute to the computational procedure of …

Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

Time series clustering is an essential unsupervised technique in cases when category
information is not available. It has been widely applied to genome data, anomaly detection …

Pattern classification has been successfully applied in many problem domains, such as
biometric recognition, document classification or medical diagnosis. Missing or unknown …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

Recently many researchers are working on cluster analysis as a main tool for exploratory
data analysis and data mining. A notable feature is that specialists in different fields of …

Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …

We propose a mechanism to use data with missing values for designing classifiers which is
different from predicting missing values for classification. Our imputation method uses an …

Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …

Let f: ℜ s↦ ℜ be a real-valued scalar field, and let x=(x 1,…, xs) T∈ ℜ s be partitioned into t
subsets of non-overlapping variables as x=(X 1,…, X t) T, with X i∈ ℜ p 1, for i= 1,…, t,∈ ti …