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Algorithms, Models and Systems for High-Performance KDD -- Collaborative Data Mining -- Organizational Data Mining -- Mining Time Series Data -- Applications -- Multimedia Data Mining -- Data Mining in Medicine -- Learning Information Patterns in Biological Databases - Stochastic Data Mining -- Data Mining for Financial Applications -- Data Mining for Intrusion Detection -- Data Mining for CRM -- Data Mining for Target Marketing -- NHECD - Nano Health and Environmental Commented Database -- Software -- Commercial Data Mining Software -- Weka-A Machine Learning Workbench for Data Mining. 330 $aThis book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are introduced. The new title of this handbook and its content reflect these changes thoroughly. Some existing chapters have been brought up to date. In addition to major revision of the existing chapters, the new edition includes totally new topics, such as: deep learning, explainable AI, human factors and social issues and advanced methods for big-data. The significant enhancement to the content reflects the growth in importance of data science. The third edition is also a timely opportunity to incorporate many other changes based on peers and students? feedback. This comprehensive handbook also presents a coherent and unified repository of data science major concepts, theories, methods, trends, challenges and applications. It covers all the crucial important machine learning methods used in data science. Today's accessibility and abundance of data make data science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for data science, methods originated from artificial intelligence, particularly machine learning, are also playing a significant role. This handbook aims to serve as the main reference for researchers in the fields of information technology, e-Commerce, information retrieval, data science, machine learning, data mining, databases and statistics as well as advanced level students studying computer science or electrical engineering. Practitioners working within these related fields and data scientists will also want to purchase this handbook as a reference. 606 $aMachine learning 606 $aArtificial intelligence 606 $aData mining 606 $aInformation storage and retrieval systems 606 $aMachine Learning 606 $aArtificial Intelligence 606 $aData Mining and Knowledge Discovery 606 $aInformation Storage and Retrieval 606 $aMineria de dades$2thub 606 $aAprenentatge automātic$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aInformation storage and retrieval systems. 615 14$aMachine Learning. 615 24$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 7$aMineria de dades. 615 7$aAprenentatge automātic. 676 $a006.312 676 $a006.312 700 $aRokach$b Lior$0620362 701 $aMaimon$b Oded$0544247 701 $aShmueli$b Erez$01424390 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739470003321 996 $aMachine Learning for Data Science Handbook$93553561 997 $aUNINA