Download Mining Very Large Databases with Parallel Processing by Alex A. Freitas PDF

By Alex A. Freitas

Mining Very huge Databases with Parallel Processing addresses the matter of large-scale information mining. it truly is an interdisciplinary textual content, describing advances within the integration of 3 desktop technology parts, specifically `intelligent' (machine learning-based) information mining recommendations, relational databases and parallel processing. the fundamental notion is to take advantage of options and strategies of the latter parts - quite parallel processing - to hurry up and scale up information mining algorithms.
The e-book is split into 3 elements. the 1st half offers a complete assessment of clever facts mining innovations akin to rule induction, instance-based studying, neural networks and genetic algorithms. Likewise, the second one half offers a entire assessment of parallel processing and parallel databases. every one of those components contains an outline of commercially-available, cutting-edge instruments. The 3rd half bargains with the applying of parallel processing to information mining. The emphasis is on discovering prevalent, good value recommendations for life like information volumes. parallel computational environments are mentioned, the 1st except using commercial-strength DBMS, and the second one utilizing parallel DBMS servers.
it truly is assumed that the reader has an information approximately such as a primary measure (BSc) in exact sciences, in order that (s)he is fairly acquainted with easy suggestions of facts and laptop technological know-how.
the first viewers for Mining Very huge Databases with ParallelProcessing is info miners and practitioners regularly, who want to practice clever info mining concepts to giant quantities of information. The publication can be of curiosity to educational researchers and postgraduate scholars, really database researchers, drawn to complex, clever database purposes, and synthetic intelligence researchers attracted to commercial, real-world purposes of computing device learning.

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Accessing large amounts of data in an unpredictable manner. In general the user is interested in data at the aggregate. summary level. Moreover. DSS applications often use aggregation. join and group by operations. which are not common in OLTP applications. and do not usually involve updates. (Updates are typically done in batch. rather than in real time). 27 Table 2-1: Different properties associated with OLTP and DSS queries. , join, group by aggregate level The requirements imposed by DSS applications are addressed by a relatively recent information-processing paradigm, called On-Line Analytical Processing (OLAP).

Continuing with our Age example. this method would produce Age intervals such as 0-10, 10-20,20-30, etc. Class-driven algorithms consider the class value when discretizing an attribute. The input for this kind of algorithm is a relation with two attributes: the attribute to be discretized and a goal attribute, whose value indicates the class to which the tuple belongs. Class-driven discretization algorithms work better than class-blind ones when the output intervals will be used by a classification algorithm.

97). One of the goals of the OLAP paradigm is to provide the user with facilities for interactive analysis. Hence, the response time of a query must be kept within a few seconds. g. [Ho et al. 96], [Agarwal et al. 96), [Ullman 96). , trading storage space for query-processing time. A list of some commercial OLAP systems can be found in [Watterson 95). However, note that a pure OLAP approach for KDD relies on a human user to perform some kind of knowledge discovery. The aim of the system is only to facilitate this discovery by providing the user with a simple and efficient way of submitting analytical queries to the database.

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