| 000 | 00921camuu22002894a 4500 | |
| 001 | 000000790664 | |
| 005 | 20021029184946 | |
| 008 | 010426s2001 mau b 001 0 eng | |
| 010 | ▼a ?01032620 | |
| 020 | ▼a 026208290X (hc. : alk. paper) | |
| 040 | ▼a DLC ▼c DLC ▼d UKM ▼d MUQ ▼d 211009 | |
| 042 | ▼a pcc | |
| 049 | 1 | ▼l 111226972 |
| 050 | 0 0 | ▼a QA76.9.D343 ▼b H38 2001 |
| 082 | 0 0 | ▼a 006.3 ▼2 21 |
| 090 | ▼a 006.3 ▼b H236p | |
| 100 | 1 | ▼a Hand, D. J. |
| 245 | 1 0 | ▼a Principles of data mining / ▼c David Hand, Heikki Mannila, Padhraic Smyth. |
| 260 | ▼a Cambridge, Mass. : ▼b MIT Press, ▼c 2001. | |
| 300 | ▼a xxxii, 546 p. ; ▼c 24 cm. | |
| 440 | 0 | ▼a Adaptive computation and machine learning |
| 500 | ▼a "A Bradford book." | |
| 504 | ▼a Includes bibliographical references (p. [491]-524) and index. | |
| 650 | 0 | ▼a Data mining. |
| 700 | 1 | ▼a Mannila, Heikki. |
| 700 | 1 | ▼a Smyth, Padhraic. |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 006.3 H236p | 등록번호 111226972 (6회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.
The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.
정보제공 :
목차
CONTENTS 1 Introduction = 1 2 Measurement and Data = 25 3 Visualizing and Exploring Data = 53 4 Data Analysis and Uncertainty = 93 5 A Systematic Overview of Data Mining Algorithms = 141 6 Models and Patterns = 165 7 Score Functions for Data Mining Algorithms = 211 8 Search and Optimization Methods = 235 9 Descriptive Modeling = 271 10 Predictive Modeling for Classification = 327 11 Predictive Modeling for Regression = 367 12 Data Organization and Databases = 399 13 Finding Patterns and Rules = 427 14 Retrieval by Content = 449