By Charu C. Aggarwal
This textbook explores the various points of information mining from the basics to the complicated info varieties and their purposes, taking pictures the broad range of challenge domain names for information mining concerns. It is going past the conventional concentrate on information mining difficulties to introduce complicated info varieties akin to textual content, time sequence, discrete sequences, spatial facts, graph facts, and social networks. formerly, no unmarried e-book has addressed most of these issues in a complete and built-in approach. The chapters of this e-book fall into one in all 3 different types:
- Fundamental chapters: information mining has 4 major difficulties, which correspond to clustering, category, organization development mining, and outlier research. those chapters comprehensively talk about a wide selection of tools for those difficulties.
- Domain chapters: those chapters talk about the categorical tools used for various domain names of information akin to textual content facts, time-series facts, series information, graph info, and spatial information.
- Application chapters: those chapters learn vital functions corresponding to move mining, net mining, rating, concepts, social networks, and privateness protection. The area chapters even have an utilized taste.
Appropriate for either introductory and complicated info mining classes, info Mining: The Textbook balances mathematical information and instinct. It comprises the required mathematical info for professors and researchers, however it is gifted in an easy and intuitive variety to enhance accessibility for college kids and commercial practitioners (including people with a constrained mathematical background). various illustrations, examples, and workouts are incorporated, with an emphasis on semantically interpretable examples.
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Extra resources for Data Mining: The Textbook
This is a dependency-oriented data type, which will be described later in this chapter. Each string is a sequence of characters (or words) corresponding to the document. However, text documents are rarely represented as strings. This is because it is diﬃcult to directly use the ordering between words in an eﬃcient way for large-scale applications, and the additional advantages of leveraging the ordering are often limited in the text domain. In practice, a vector-space representation is used, where the frequencies of the words in the document are used for analysis.
The behavioral attribute is a categorical value. Therefore, discrete sequence data are deﬁned in a similar way to time-series data. 3 (Multivariate Discrete Sequence Data) A discrete sequence of length n and dimensionality d contains d discrete feature values at each of n diﬀerent time stamps t1 . . tn . Each of the n components Yi contains d discrete behavioral attributes (yi1 . . yid ), collected at the ith time-stamp. For example, consider a sequence of Web accesses, in which the Web page address and the originating IP address of the request are collected for 100 diﬀerent accesses.
In fact, a simple methodology to determine outliers uses clustering as an intermediate step. Some examples of relevant applications are as follows: • Intrusion-detection systems: In many networked computer systems, diﬀerent kinds of data are collected about the operating system calls, network traﬃc, or other activity in the system. These data may show unusual behavior because of malicious activity. The detection of such activity is referred to as intrusion detection. • Credit card fraud: Unauthorized use of credit cards may show diﬀerent patterns, such as a buying spree from geographically obscure locations.