標題: Titlebook: Data Mining and Knowledge Discovery Handbook; Oded Maimon,Lior Rokach Book 20102nd edition Springer Science+Business Media, LLC 2010 Bayes [打印本頁] 作者: 爆發(fā) 時間: 2025-3-21 18:16
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書目名稱Data Mining and Knowledge Discovery Handbook讀者反饋
書目名稱Data Mining and Knowledge Discovery Handbook讀者反饋學(xué)科排名
作者: 小爭吵 時間: 2025-3-21 20:23
Data Cleansing: A Prelude to Knowledge Discovery surveyed and reviewed and a brief overview of existing data cleansing tools is given. A general framework of the data cleansing process is presented as well as a set of general methods that can be used to address the problem. The applicable methods include statistical outlier detection, pattern mat作者: Altitude 時間: 2025-3-22 03:49 作者: intoxicate 時間: 2025-3-22 07:15 作者: dura-mater 時間: 2025-3-22 12:18
Dimension Reduction and Feature Selectionge data sets. Reducing dimensionality (the number of attributed or the number of records) can effectively cut this cost. This chapter focuses a pre-processing step which removes dimension from a given data set before it is fed to a data mining algorithm. This work explains how it is often possible t作者: ALOFT 時間: 2025-3-22 13:03 作者: ALOFT 時間: 2025-3-22 20:41 作者: PHAG 時間: 2025-3-23 00:30
Supervised Learningsed in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.作者: LAPSE 時間: 2025-3-23 03:47 作者: 兩種語言 時間: 2025-3-23 09:22
Bayesian Networksntal aspects of Bayesian networks and some of their technical aspects, with a particular emphasis on the methods to induce Bayesian networks from different types of data. Basic notions are illustrated through the detailed descriptions of two Bayesian network applications: one to survey data and one 作者: keloid 時間: 2025-3-23 12:12 作者: fatty-acids 時間: 2025-3-23 17:51
Support Vector Machinesclassifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes, while maximizing the distance to the nearest cleanly split examples. The parameters of the solution hyperplane are derived from a quadratic programming optimization problem. Here, w作者: 1分開 時間: 2025-3-23 21:26
Rule Inductionule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanc作者: dearth 時間: 2025-3-24 00:47 作者: 代理人 時間: 2025-3-24 02:44 作者: 動脈 時間: 2025-3-24 09:11 作者: 策略 時間: 2025-3-24 11:27
Outlier Detectionnivariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for Data Mining is often based on distance measures, clustering and spatial methods.作者: carbohydrate 時間: 2025-3-24 15:31
Supervised Learningsed in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.作者: Communal 時間: 2025-3-24 23:05
Bayesian Networksntal aspects of Bayesian networks and some of their technical aspects, with a particular emphasis on the methods to induce Bayesian networks from different types of data. Basic notions are illustrated through the detailed descriptions of two Bayesian network applications: one to survey data and one to marketing data.作者: 窩轉(zhuǎn)脊椎動物 時間: 2025-3-25 00:38
Data Mining within a Regression Frameworkedures are discussed within a regression framework. These include non-parametric smoothers, classification and regression trees, bagging, and random forests. In each case, the goal is to characterize one or more of the distributional features of a response conditional on a set of predictors.作者: 精美食品 時間: 2025-3-25 03:34
Rule Inductionule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanced methods are listed.作者: 可卡 時間: 2025-3-25 09:30 作者: 樹膠 時間: 2025-3-25 14:24
Frequent Set MiningFrequent sets lie at the basis of many Data Mining algorithms. As a result, hundreds of algorithms have been proposed in order to solve the frequent set mining problem. In this chapter, we attempt to survey the most successful algorithms and techniques that try to solve this problem efficiently.作者: Osteoarthritis 時間: 2025-3-25 17:19 作者: Feature 時間: 2025-3-25 22:50 作者: 物質(zhì) 時間: 2025-3-26 00:35 作者: 種子 時間: 2025-3-26 06:35 作者: 上流社會 時間: 2025-3-26 10:17 作者: 宏偉 時間: 2025-3-26 16:17
HPV Detection and Clinical Implicationsrning from qualitative data. Discretization addresses this issue by transforming quantitative data into qualitative data. This chapter presents a comprehensive introduction to discretization. It clarifies the definition of discretization. It provides a taxonomy of discretization methods together wit作者: 出價 時間: 2025-3-26 17:39
Mike Anderson: The Kid Who Wanted to Flynivariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for Data Mining is often based on distance measures, clustering and spatial methods.作者: 親愛 時間: 2025-3-26 21:24 作者: 滔滔不絕的人 時間: 2025-3-27 04:06 作者: stressors 時間: 2025-3-27 07:57
Mark E. Polhemus MD,Kent E. Kester MDntal aspects of Bayesian networks and some of their technical aspects, with a particular emphasis on the methods to induce Bayesian networks from different types of data. Basic notions are illustrated through the detailed descriptions of two Bayesian network applications: one to survey data and one 作者: 積極詞匯 時間: 2025-3-27 11:21 作者: Banister 時間: 2025-3-27 13:35 作者: MIRE 時間: 2025-3-27 18:44
Primary Groups and the Regimental Systemule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanc作者: 熱心助人 時間: 2025-3-27 22:01 作者: 使厭惡 時間: 2025-3-28 06:02 作者: FACET 時間: 2025-3-28 10:16 作者: 搜集 時間: 2025-3-28 11:30 作者: INCH 時間: 2025-3-28 15:50
https://doi.org/10.1007/978-1-349-09556-8l, and understandable patterns from large and complex data sets. . (DM) is the core of the KDD process, involving the inferring of algorithms that explore the data, develop the model and discover previously unknown patterns. The model is used for understanding phenomena from the data, analysis and prediction.作者: pineal-gland 時間: 2025-3-28 20:35
Mike Anderson: The Kid Who Wanted to Flynivariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for Data Mining is often based on distance measures, clustering and spatial methods.作者: 機制 時間: 2025-3-29 01:38 作者: CHARM 時間: 2025-3-29 03:28 作者: 形容詞 時間: 2025-3-29 10:20 作者: cunning 時間: 2025-3-29 12:47
Primary Groups and the Regimental Systemule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanced methods are listed.作者: 過度 時間: 2025-3-29 18:06 作者: CROAK 時間: 2025-3-29 22:42 作者: Intend 時間: 2025-3-30 02:16
Book 20102nd editionh in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security..Data Mining and Knowledge Discovery Handbook, Second Edition. is designed for research scientists, lib作者: engrossed 時間: 2025-3-30 04:06
s including finance, marketing, medicine, biology, engineering, telecommunications, software, and security..Data Mining and Knowledge Discovery Handbook, Second Edition. is designed for research scientists, lib978-0-387-09823-4作者: buoyant 時間: 2025-3-30 10:57
Book 20102nd editionTo be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data..Data Mining and Knowledge Discovery Han作者: Reverie 時間: 2025-3-30 15:36
Handling Missing Attribute Values with all known attribute values. In parallel methods, there is no preprocessing, i.e., knowledge is acquired directly from the original data sets. In this chapter the main emphasis is put on rule induction. Methods of handling attribute values for decision tree generation are only briefly summarized.作者: Postulate 時間: 2025-3-30 20:34
Dimension Reduction and Feature Selectionocessing step which removes dimension from a given data set before it is fed to a data mining algorithm. This work explains how it is often possible to reduce dimensionality with minimum loss of information. Clear dimension reduction taxonomy is described and techniques for dimension reduction are presented theoretically.作者: 食道 時間: 2025-3-30 22:41 作者: PAN 時間: 2025-3-31 02:26 作者: Spinal-Fusion 時間: 2025-3-31 07:17 作者: 流利圓滑 時間: 2025-3-31 11:33 作者: Conjuction 時間: 2025-3-31 15:06
