By Charu C. Aggarwal
This textbook explores the several points of knowledge mining from the basics to the advanced info forms and their functions, shooting the large variety of challenge domain names for information mining matters. It is going past the conventional specialize in info mining difficulties to introduce complex information forms resembling textual content, time sequence, discrete sequences, spatial facts, graph information, and social networks. in the past, no unmarried publication has addressed these kinds of subject matters in a entire and built-in approach. The chapters of this booklet fall into one in every of 3 different types:
- Fundamental chapters: facts mining has 4 major difficulties, which correspond to clustering, class, organization development mining, and outlier research. those chapters comprehensively speak about a wide selection of equipment for those difficulties.
- Domain chapters: those chapters speak about the categorical equipment used for various domain names of information equivalent to textual content info, time-series information, series facts, graph information, and spatial information.
- Application chapters: those chapters research vital purposes equivalent to circulation mining, net mining, rating, innovations, social networks, and privateness upkeep. The area chapters even have an utilized taste.
Appropriate for either introductory and complex info mining classes, information Mining: The Textbook balances mathematical info and instinct. It includes the required mathematical information for professors and researchers, however it is gifted in an easy and intuitive sort to enhance accessibility for college students and business practitioners (including people with a restricted mathematical background). a number of illustrations, examples, and routines are incorporated, with an emphasis on semantically interpretable examples.
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Extra info for Data Mining: The Textbook
11 F East Indian 10547 Jack M. 56 M Caucasian 10562 Wei L. 1 Nondependency-Oriented Data This is the simplest form of data and typically refers to multidimensional data. This data typically contains a set of records. A record is also referred to as a data point, instance, example, transaction, entity, tuple, object, or feature-vector, depending on the application at hand. Each record contains a set of ﬁelds, which are also referred to as attributes, dimensions, and features. These terms will be used interchangeably throughout this book.
Furthermore, Li may be speciﬁed in terms of precise spatial coordinates, such as latitude and longitude, or in terms of a logical location, such as the city or state. Spatial data mining is closely related to time-series data mining, in that the behavioral attributes in most commonly studied spatial applications are continuous, although some applications may use categorical attributes as well. Therefore, value continuity is observed across contiguous spatial locations, just as value continuity is observed across contiguous time stamps in time-series data.
For example, the adjacent values recorded by a temperature sensor will usually vary smoothly over time, and this factor needs to be explicitly used in the data mining process. The nature of the temporal dependency may vary signiﬁcantly with the application. For example, some forms of sensor readings may show periodic patterns of the measured 10 CHAPTER 1. AN INTRODUCTION TO DATA MINING attribute over time. An important aspect of time-series mining is the extraction of such dependencies in the data.
Data Mining: The Textbook by Charu C. Aggarwal