Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fe
Clustering is the unsupervised classification of patterns (observations, data items, or feature vect...
Clustering is the task of organizing a set of objects into meaningful groups. These groups can be di...
Abstract The chapter by Milligan and Hirtle provides an overview of the current state of knowledge i...
Clustering is a division of data into groups of similar objects. Representing the data by fewer clus...
This is the first book to take a truly comprehensive look at clustering. It begins with an introduct...
Clustering is one of the most important techniques in data mining. This chapter presents a survey of...
International audienceThis special issue is particularly focused on fundamental and practical issues...
Cluster Analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneou...
Clustering has primarily been used as an analytical technique to group unlabeled data for extracting...
Abstract: Clustering is the assignment of data objects (records) into groups (called clusters) so th...
Abstract. This talk is an attempt at structuring and systematising the develop-ment of clustering as...
This chapter provides a comprehensive, focused introduction to clustering, viewed as a fundamental m...
We examine whether the quality of dierent clustering algorithms can be compared by a general, scient...
Data mining is a new discipline lying at the interface of statistics, database technology, pattern ...
Clustering is the classification of objects into different groups, or more precisely, the partitioni...
Clustering is the unsupervised classification of patterns (observations, data items, or feature vect...
Clustering is the task of organizing a set of objects into meaningful groups. These groups can be di...
Abstract The chapter by Milligan and Hirtle provides an overview of the current state of knowledge i...
Clustering is a division of data into groups of similar objects. Representing the data by fewer clus...
This is the first book to take a truly comprehensive look at clustering. It begins with an introduct...
Clustering is one of the most important techniques in data mining. This chapter presents a survey of...
International audienceThis special issue is particularly focused on fundamental and practical issues...
Cluster Analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneou...
Clustering has primarily been used as an analytical technique to group unlabeled data for extracting...
Abstract: Clustering is the assignment of data objects (records) into groups (called clusters) so th...
Abstract. This talk is an attempt at structuring and systematising the develop-ment of clustering as...
This chapter provides a comprehensive, focused introduction to clustering, viewed as a fundamental m...
We examine whether the quality of dierent clustering algorithms can be compared by a general, scient...
Data mining is a new discipline lying at the interface of statistics, database technology, pattern ...
Clustering is the classification of objects into different groups, or more precisely, the partitioni...
Clustering is the unsupervised classification of patterns (observations, data items, or feature vect...
Clustering is the task of organizing a set of objects into meaningful groups. These groups can be di...
Abstract The chapter by Milligan and Hirtle provides an overview of the current state of knowledge i...