By Jiawei Han, Micheline Kamber, Jian Pei
The expanding quantity of knowledge in smooth enterprise and technological know-how demands extra complicated and complex instruments. even if advances in facts mining know-how have made vast facts assortment a lot more uncomplicated, it's nonetheless constantly evolving and there's a consistent desire for brand new thoughts and instruments that may aid us rework this information into priceless details and knowledge.
Since the former edition's ebook, nice advances were made within the box of information mining. not just does the 3rd of variation of Data Mining: innovations and Techniques proceed the culture of equipping you with an realizing and alertness of the speculation and perform of gaining knowledge of styles hidden in huge info units, it additionally specializes in new, very important subject matters within the box: information warehouses and information dice expertise, mining circulate, mining social networks, and mining spatial, multimedia and different advanced facts. each one bankruptcy is a stand-alone advisor to a severe subject, offering confirmed algorithms and sound implementations able to be used without delay or with strategic amendment opposed to stay info. this can be the source you would like which will observe today's strongest facts mining suggestions to satisfy genuine enterprise challenges.
• provides dozens of algorithms and implementation examples, all in pseudo-code and compatible to be used in real-world, large-scale info mining projects.
• Addresses complicated themes comparable to mining object-relational databases, spatial databases, multimedia databases, time-series databases, textual content databases, the realm vast internet, and functions in different fields.
• offers a accomplished, functional examine the thoughts and strategies you want to get the main from your info
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Additional info for Data Mining: Concepts and Techniques: Concepts and Techniques (3rd Edition)
Therefore, we adopt a broad view of data mining functionality: Data mining is the process of discovering interesting patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the Web, other information repositories, or data that are streamed into the system dynamically. 3 What Kinds of Data Can Be Mined? As a general technology, data mining can be applied to any kind of data as long as the data are meaningful for a target application. 3). The concepts and techniques presented in this book focus on such data.
The widening gap between data and information calls for the systematic development of data mining tools that can turn data tombs into “golden nuggets” of knowledge. 2 What Is Data Mining? It is no surprise that data mining, as a truly interdisciplinary subject, can be defined in many different ways. Even the term data mining does not really present all the major components in the picture. To refer to the mining of gold from rocks or sand, we say gold mining instead of rock or sand mining. 3 Data mining—searching for knowledge (interesting patterns) in data.
Sources for further information are given in the bibliographic notes. 4 Cluster Analysis Unlike classification and regression, which analyze class-labeled (training) data sets, clustering analyzes data objects without consulting class labels. In many cases, classlabeled data may simply not exist at the beginning. 10 A 2-D plot of customer data with respect to customer locations in a city, showing three data clusters. class labels for a group of data. The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity.