Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Subspace clustering is an extension of traditional clustering that seeks to find clusters in
different subspaces within a dataset. Often in high dimensional data, many dimensions are …

Among many clustering algorithms, the K-means clustering algorithm is widely used
because of its simple algorithm and fast convergence. However, the K-value of clustering …

Finding useful patterns in large datasets has attracted considerable interest recently, and
one of the most widely studied problems in this area is the identification of clusters, or …

Clustering is the division of data into groups of similar objects. In clustering, some details are
disregarded in exchange for data simplification. Clustering can be viewed as a data …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

This chapter provides an overview of the data matching process, and describes the five
major steps involved in this process: data pre-processing (cleaning and standardisation) …

Metagenomic datasets contain billions of protein sequences that could greatly enhance
large-scale functional annotation and structure prediction. Utilizing this enormous resource …

Often, in the real world, entities have two or more representations in databases. Duplicate
records do not share a common key and/or they contain errors that make duplicate matching …

Twitter is among the fastest‐growing microblogging and online social networking services.
Messages posted on Twitter (tweets) have been reporting everything from daily life stories to …