標(biāo)題: Titlebook: Data Preprocessing in Data Mining; Salvador García,Julián Luengo,Francisco Herrera Book 2015 Springer International Publishing Switzerland [打印本頁(yè)] 作者: 不友善 時(shí)間: 2025-3-21 19:29
書(shū)目名稱Data Preprocessing in Data Mining影響因子(影響力)
書(shū)目名稱Data Preprocessing in Data Mining影響因子(影響力)學(xué)科排名
書(shū)目名稱Data Preprocessing in Data Mining網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Data Preprocessing in Data Mining網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Data Preprocessing in Data Mining被引頻次
書(shū)目名稱Data Preprocessing in Data Mining被引頻次學(xué)科排名
書(shū)目名稱Data Preprocessing in Data Mining年度引用
書(shū)目名稱Data Preprocessing in Data Mining年度引用學(xué)科排名
書(shū)目名稱Data Preprocessing in Data Mining讀者反饋
書(shū)目名稱Data Preprocessing in Data Mining讀者反饋學(xué)科排名
作者: 逢迎白雪 時(shí)間: 2025-3-21 20:41 作者: objection 時(shí)間: 2025-3-22 04:19
https://doi.org/10.1007/978-1-4613-0429-6ion (Sect.?.) and the latest Machine Learning based approaches which use algorithms for classification or regression in order to accomplish the imputation (Sect.?.). Finally a comparative experimental study will be carried out in Sect.?..作者: Regurgitation 時(shí)間: 2025-3-22 07:21 作者: Expressly 時(shí)間: 2025-3-22 08:53 作者: epicardium 時(shí)間: 2025-3-22 14:07 作者: epicardium 時(shí)間: 2025-3-22 20:38 作者: ABOUT 時(shí)間: 2025-3-22 22:01
Data Sets and Proper Statistical Analysis of Data Mining Techniques,to alleviate the problematic associated to the validation of any supervised method as well as the details of the performance measures that will be used in the rest of the book. Section?. takes a tour of the most common statistical techniques required in the literature to provide meaningful and corre作者: Arresting 時(shí)間: 2025-3-23 02:39
Dealing with Missing Values,ion (Sect.?.) and the latest Machine Learning based approaches which use algorithms for classification or regression in order to accomplish the imputation (Sect.?.). Finally a comparative experimental study will be carried out in Sect.?..作者: Indicative 時(shí)間: 2025-3-23 07:47
Dealing with Noisy Data,rom this point on, the two main approaches carried out in the literature are described. On the first hand, modifying and cleaning the data is studied in Sect.?., whereas designing noise robust Machine Learning algorithms is tackled in Sect.?.. An empirical comparison between the latest approaches in作者: MONY 時(shí)間: 2025-3-23 13:11 作者: 不能妥協(xié) 時(shí)間: 2025-3-23 15:24
A Data Mining Software Package Including Data Preparation and Reduction: KEEL,in features and usage. For the practitioners interest, the most common used data sources are introduced in Sect.?. and the steps needed to integrate any new algorithm in it in Sect.?.. Once the results have been obtained, the appropriate comparison guidelines are provided in Sect.?.. The most import作者: 厭惡 時(shí)間: 2025-3-23 19:16 作者: 羊欄 時(shí)間: 2025-3-24 00:00
978-3-319-37731-5Springer International Publishing Switzerland 2015作者: Intruder 時(shí)間: 2025-3-24 03:52
Salvador García,Julián Luengo,Francisco HerreraCovers the set of techniques under the umbrella of data preprocessing in data mining and machine learning.A comprehensive book devoted completely to preprocessing in data mining.Written by experts in 作者: 蝕刻術(shù) 時(shí)間: 2025-3-24 07:16
Intelligent Systems Reference Libraryhttp://image.papertrans.cn/d/image/262990.jpg作者: Pantry 時(shí)間: 2025-3-24 13:26
Data Preprocessing in Data Mining978-3-319-10247-4Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: 馬具 時(shí)間: 2025-3-24 18:01
https://doi.org/10.1007/978-1-4613-0429-6nts of the rest of the book will be introduced, such as learning models, strategies and paradigms, etc. Thus, the whole process known as Knowledge Discovery in Data is provided in Sect.?.. A review on the main models of Data Mining is given in Sect.?., accompanied a clear differentiation between Sup作者: MAG 時(shí)間: 2025-3-24 22:51
https://doi.org/10.1007/978-1-4613-0429-6a sets are available and widely used to check the performance of the technique being considered. Many of the subsequent sections of this book include a practical experimental comparison of the techniques described in each one as a exemplification of this process. Such comparisons require a clear bed作者: 確定 時(shí)間: 2025-3-25 03:15 作者: intelligible 時(shí)間: 2025-3-25 06:51
https://doi.org/10.1007/978-1-4613-0429-6nformation is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formally known as imputation. After作者: thrombosis 時(shí)間: 2025-3-25 09:34
Communications Standard Dictionarynces in classification problems. Noise is an unavoidable problem, which affects the data collection and data preparation processes in Data Mining applications, where errors commonly occur. The performance of the models built under such circumstances will heavily depend on the quality of the training作者: accrete 時(shí)間: 2025-3-25 14:29
https://doi.org/10.1007/978-1-4613-0429-6 and attributes; and simplifying the domain of the data. A global overview to this respect is given in Sect.?.. One of the well-known problems in Data Mining is the “curse of dimensionality”, related with the usual high amount of attributes in data. Section?. deals with this problem. Data sampling a作者: epicondylitis 時(shí)間: 2025-3-25 18:28 作者: CLOUT 時(shí)間: 2025-3-25 21:08
https://doi.org/10.1007/978-1-4613-0429-6t of all, we define a broader perspective on concepts and topics related with instance selection (Sect.?.). Due to the fact that instance selection has been distinguished over the years as two type of tasks, depending on the data mining method applied later, we clearly separate it into two processes作者: Lineage 時(shí)間: 2025-3-26 03:46
Communications Standard Dictionarycontinuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data. An overview of discretization together with a complete outlook and taxonomy are supplied in Sects.?. and?.. We conduct an experimental study in supe作者: 全等 時(shí)間: 2025-3-26 06:52 作者: aqueduct 時(shí)間: 2025-3-26 09:29
Introduction,nts of the rest of the book will be introduced, such as learning models, strategies and paradigms, etc. Thus, the whole process known as Knowledge Discovery in Data is provided in Sect.?.. A review on the main models of Data Mining is given in Sect.?., accompanied a clear differentiation between Sup作者: 水汽 時(shí)間: 2025-3-26 15:25 作者: happiness 時(shí)間: 2025-3-26 19:15
Data Preparation Basic Models,pic is given in Sect.?.. When there are several or heterogeneous sources of data, an integration of the data is needed to be performed. This task is discussed in Sect.? .. After the data is computer readable and constitutes an unique source, it usually goes through a cleaning phase where the data in作者: ENNUI 時(shí)間: 2025-3-26 23:49 作者: 保守黨 時(shí)間: 2025-3-27 02:08
Dealing with Noisy Data,nces in classification problems. Noise is an unavoidable problem, which affects the data collection and data preparation processes in Data Mining applications, where errors commonly occur. The performance of the models built under such circumstances will heavily depend on the quality of the training作者: 屈尊 時(shí)間: 2025-3-27 07:14
Data Reduction, and attributes; and simplifying the domain of the data. A global overview to this respect is given in Sect.?.. One of the well-known problems in Data Mining is the “curse of dimensionality”, related with the usual high amount of attributes in data. Section?. deals with this problem. Data sampling a作者: ineptitude 時(shí)間: 2025-3-27 10:17 作者: 試驗(yàn) 時(shí)間: 2025-3-27 14:56
Instance Selection,t of all, we define a broader perspective on concepts and topics related with instance selection (Sect.?.). Due to the fact that instance selection has been distinguished over the years as two type of tasks, depending on the data mining method applied later, we clearly separate it into two processes作者: GEST 時(shí)間: 2025-3-27 18:51 作者: habitat 時(shí)間: 2025-3-28 00:10 作者: 做作 時(shí)間: 2025-3-28 04:33 作者: STALL 時(shí)間: 2025-3-28 09:38 作者: Predigest 時(shí)間: 2025-3-28 14:24
Communications Standard Dictionary. An overview of discretization together with a complete outlook and taxonomy are supplied in Sects.?. and?.. We conduct an experimental study in supervised classification involving the most representative discretizers, different types of classifiers, and a large number of data sets (Sect.?.).作者: Keshan-disease 時(shí)間: 2025-3-28 16:37
Book 2015ctly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to ana作者: Harbor 時(shí)間: 2025-3-28 18:58
Data Reduction, Mining is the “curse of dimensionality”, related with the usual high amount of attributes in data. Section?. deals with this problem. Data sampling and data simplification are introduced in Sects.?. and ., respectively, providing the basic notions on these topics for further analysis and explanation in subsequent chapters of the book.作者: CHARM 時(shí)間: 2025-3-29 00:12 作者: HATCH 時(shí)間: 2025-3-29 07:05
https://doi.org/10.1007/978-1-4613-0429-6accuracies are corrected. Section? . focuses in the latter task. Finally, some Data Mining applications involve some particular constraints like ranges for the data features, which may imply the normalization of the features (Sect.?.) or the transformation of the features of the data distribution (Sect.?.).作者: 粗鄙的人 時(shí)間: 2025-3-29 10:10
https://doi.org/10.1007/978-1-4613-0429-6 problems that assume more complexity or hybridizations with respect to the classical learning paradigms. Finally, we establish the relationship between Data Preprocessing with Data Mining in Sect.?..作者: 大量 時(shí)間: 2025-3-29 13:06
https://doi.org/10.1007/978-1-4613-0429-6ter optimization models and derivatives methods related with feature selection, Sect.?. provides a summary on related and advanced topics, such as feature construction and feature extraction. An enumeration of some comparative experimental studies conducted in the specialized literature is included in Sect.?..作者: 前奏曲 時(shí)間: 2025-3-29 15:41
Introduction, problems that assume more complexity or hybridizations with respect to the classical learning paradigms. Finally, we establish the relationship between Data Preprocessing with Data Mining in Sect.?..
