Data Modeling Design

Read e-book online Agent intelligence through data mining PDF

By Andreas L. Symeonidis

ISBN-10: 0387233431

ISBN-13: 9780387233437

Wisdom, hidden in voluminous info repositories generally created and maintained through todays purposes, will be extracted by way of information mining. the next move is to rework this stumbled on wisdom into the inference mechanisms or just the habit of brokers and multi-agent platforms. Agent Intelligence via facts Mining addresses this factor, in addition to the controversial problem of producing intelligence from info whereas moving it to a separate, probably self reliant, software program entity. This booklet includes a technique, instruments and strategies, and a number of other examples of agent-based functions built with this procedure. This quantity focuses in general at the use of information mining for smarter, extra effective brokers.

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The most common transformation techniques are: • Smoothing, which removes noise from data. • Aggregation, which summarizes data and constructs data cubes. • Generalization, which is also known as concept hierarchy climbing. • Attribute/feature construction, which composes new attributes from the given ones. • Normalization, which scales the data within a small, specified range. The most dominant normalization techniques according to Weiss and Indurkhya are [Weiss and Indurkhya, 1998]: 1) min-max normalization: Linear transformation is applied on the data.

Let there be N d-dimensional data tuples or data objects O{ in a subcluster. CF summarizes information about subclusters of objects and is defined as a triplet (iV, LS, SS), where TV is the number of data objects residing within the cluster, LS is the sum of the data objects (J2i=i °i) a n d SS is their square sum (52,i=i °f)• The CF-tree is a weighted tree with two parameters: the branching factor B and threshold T. B determines the maximum number of children a tree node may have, while T determines the maximum diameter of a subcluster residing in the leaves.

4. Reduce and project data Find useful features to represent the data, depending on the goal of the task. 5. g. summarization, classification, regression, clustering, etc. 6. Choose a data mining algorithm Select method(s) to be used for searching for patterns in the data. 7. Apply data mining 8. Evaluate data mining results Interpret mined patterns, possibly return to steps 1-7 for further iteration. Data Mining and Knowledge Discovery: A brief overview 15 9. Consolidate discovered knowledge Incorporate this knowledge into another system for further action, or simply document it and report it to interested parties.

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Agent intelligence through data mining by Andreas L. Symeonidis

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