Agent intelligence through data mining by Andreas L. Symeonidis PDF

By Andreas L. Symeonidis

ISBN-10: 0387233431

ISBN-13: 9780387233437

Wisdom, hidden in voluminous information repositories in many instances created and maintained by way of todays functions, might be extracted by means of facts mining. your next step is to rework this chanced on wisdom into the inference mechanisms or just the habit of brokers and multi-agent structures. 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, most likely self reliant, software program entity. This publication encompasses a technique, instruments and methods, and a number of other examples of agent-based purposes constructed with this process. This quantity focuses quite often at the use of knowledge mining for smarter, extra effective brokers.

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1 A GENT INTELLIGENCE THROUGH DATA MINING Data Preprocessing The Scope of Data Preprocessing In order to achieve the maximum benefit from the application of a DM algorithm on a dataset, preprocessing of the data is necessary, to ensure data integrity and validity. , 1998; Pyle, 1999]. A brief overview of these tasks follows. 2 Data Cleaning Real-world data are usually noisy and incomplete, and their cleaning includes all the processes offillingin missing values, smoothing out noise, and discovering outliers.

2 Genetic Algorithm Terminology All the terms of GAs originate from the fields of biology and genetics [Goldberg, 1989]. For example, as already mentioned, problem parameters are encoded as a bit sequence. This bit sequence that comprises the string is analogous to the chromosomes in biology. In nature, two or more chromosomes are combined to finally create the genotype. In GAs, genotypes are mapped to the structure, the set of available strings. In nature, the organism developed when the genetic code interacts with the surrounding environment, is called a phenotype, which in the case of GAs corresponds to the decoded structure of the problem, aiming to create a candidate solution space.

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.

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