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[null,"1.","Record Nr.",null,"UNINA9910451300103321"]
[null,null,"Titolo",null,"Lecture notes in data mining [[electronic resource] /] / edited by Michael W. Berry, Murray Browne"]
[null,null,"Pubbl/distr/stampa",null,"Hackensack, NJ, : World Scientific, c2006"]
[null,null,"ISBN",null,"1-281-37904-2"]
[{"$nulls":4},"9786611379049"]
[{"$nulls":4},"981-277-363-0"]
[null,null,"Descrizione fisica",null,"1 online resource (238 p.)"]
[null,null,"Disciplina",null,"005.741"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Database searching"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"CONTENTS               ; Preface              ; 1 Point Estimation Algorithms                                    ; 1. Introduction                      ; 2. Motivation                    ; 3. Methods of Point Estimation                                     ; 4. Measures of Performance                                 ; 5. Summary                 ; 2 Applications of Bayes Theorem                                      ; 1. Introduction                      ; 2. Motivation                    ; 3. The Bayes Approach for Classification"]
[{"$nulls":4},"4. Examples                  5. Summary                 ; 3 Similarity Measures                            ; 1. Introduction                      ; 2. Motivation                    ; 3. Classic Similarity Measures                                     ; 4. Example                 ; 5. Current Applications                              ; 6. Summary                 ; 4 Decision Trees                       ; 1. Introduction                      ; 2. Motivation                    ; 3. Decision Tree Algorithms"]
[{"$nulls":4},"4. Example: Classification of University Students                                                        5. Applications of Decision Tree Algorithms                                                  ; 6. Summary                 ; 5 Genetic Algorithms                           ; 1. Introduction                      ; 2. Motivation  ; 3. Fundamentals                      ; 4. Example: The Traveling-Salesman                                         ; 5. Current and Future Applications                                         ; 6. Summary"]
[{"$nulls":4},"6 Classification: Distance-based Algorithms                                                  1. Introduction                      ; 2. Motivation                    ; 3. Distance Functions                            ; 4. Classification Algorithms                                   ; 5. Current Applications                              ; 6. Summary                 ; 7 "]
[{"$nulls":4},"Decision Tree-based Algorithms                                       ; 1. Introduction                      ; 2. Motivation                    ; 3. ID3             ; 4. C4.5              ; 5. C5.0"]
[{"$nulls":4},"6. CART              7. Summary                 ; 8 Covering (Rule-based) Algorithms                                         ; 1. Introduction                      ; 2. Motivation                    ; 3. Classification Rules                              ; 4. Covering (Rule-based) Algorithms                                          ; 5. Applications of Covering Algorithms                                             ; 6. Summary                 ; 9 Clustering: An Overview                                ; 1. Introduction                      ; 2. Motivation"]
[{"$nulls":4},"3. The Clustering Process"]
[null,null,null,"Sommario/riassunto","The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field.  This book is a series of seventeen edited \"student-authored lectures\" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight.  The initial chapters lay a framework of data mining techniques by expla"]