By Andreas L. Symeonidis
Wisdom, hidden in voluminous info repositories frequently created and maintained by means of todays purposes, should be extracted via info mining. your next step is to rework this came upon 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 debatable problem of producing intelligence from info whereas shifting it to a separate, very likely self sufficient, software program entity. This e-book encompasses a method, instruments and methods, and a number of other examples of agent-based purposes constructed with this method. This quantity focuses generally at the use of information mining for smarter, extra effective brokers.
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Additional resources for Agent intelligence through data mining
Classification The discovery of a knowledge model that classifies new data into one of the existing pre-specified classes. 2. Characterization and Discrimination The discovery of a valid description for a part of the dataset. 3. Clustering The identification of a finite number of clusters that group data based on their similarities and differences. 4. Association-Correlation The extraction of association rules, which indicate cause-effect relations between the attributes of a dataset. 5. Outlier analysis The identification and exclusion of data that do not abide by the behavior of the rest of the data records.
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.
Since we are going to use them extensively in subsequent chapters, we present the ID3 algorithm in more detail, so that the unfamiliar reader can better comprehend the way DTs are built. 1 AGENT INTELLIGENCE THROUGH DATA MINING The ID3 algorithm ID3 aims to minimize the number of iterations during classification of a new data tuple and can be applied only to categorical data. During the tree building phase, ID3 follows a top-down approach, which comprises the following three steps: a) One of the dataset attributes is selected as the root node, and attribute values become tree branches.
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