Reverse Clustering: Formulation, Interpretation and Case Studies (Studies in Computational Intelligence)

Owsi?ski, Jan W.; Sta?czak, Jaros?aw; Opara, Karol; Zadro?ny, S?awomir; Kacprzyk, Janusz

ISBN 10: 3030693619 / ISBN 13: 9783030693619

Editore: Springer, 2022

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1st ed. 2021 edition NO-PA16APR2015-KAP.

Codice articolo 26394680368

Dati bibliografici

• Titolo: Reverse Clustering: Formulation, Interpretation and Case Studies (Studies in Computational Intelligence)
• Editore: Springer
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3030693619
• ISBN 13: 9783030693619
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

This book presents a new perspective on and a new approach to a wide spectrum of situations, related to data analysis, actually, a kind of a new paradigm. Namely, for a given data set and its partition, whose origins may be of any kind, the authors try to reconstruct this partition on the basis of the data set given, using very broadly conceived clustering procedure. The main advantages of this new paradigm concern the substantive aspects of the particular cases considered, mainly in view of the variety of interpretations, which can be assumed in the framework of the paradigm. Due to the novel problem formulation and the flexibility in the interpretations of this problem and its components, the domains, which are encompassed (or at least affected) by the potential use of the paradigm, include cluster analysis, classification, outlier detection, feature selection, and even factor analysis as well as geometry of the data set. The book is useful for all those who look for new, nonconventional approaches to their data analysis problems.

Dalla quarta di copertina

This book presents a new perspective on and a new approach to a wide spectrum of situations, related to data analysis, actually, a kind of a new paradigm. Namely, for a given data set and its partition, whose origins may be of any kind, the authors try to reconstruct this partition on the basis of the data set given, using very broadly conceived clustering procedure. The main advantages of this new paradigm concern the substantive aspects of the particular cases considered, mainly in view of the variety of interpretations, which can be assumed in the framework of the paradigm. Due to the novel problem formulation and the flexibility in the interpretations of this problem and its components, the domains, which are encompassed (or at least affected) by the potential use of the paradigm, include cluster analysis, classification, outlier detection, feature selection, and even factor analysis as well as geometry of the data set. The book is useful for all those who look for new, nonconventional approaches to their data analysis problems.<br>