Pattern recognition algorithms for data mining : by Sankar K Pal; Pabitra Mitra

By Sankar K Pal; Pabitra Mitra

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Other hybridizations typically enjoy the generic and application-specific merits of the individual soft computing tools that they integrate. Data mining functions modeled by such systems include rule extraction, data summarization, clustering, incorporation of domain knowledge, and partitioning. Casebased reasoning (CBR), a novel AI problem-solving paradigm, has recently drawn the attention of both soft computing and data mining communities. A profile of its theory, algorithms, and potential applications is available in [262, 195, 208].

For time complexity, the appropriate algorithmic question is what is the growth rate of the algorithm’s run time as the number of examples and their dimensions increase? As may be expected, time-complexity analysis does not tell the whole story. As the number of instances grows, space constraints become critical, since, almost all existing implementations of a learning algorithm operate with training set entirely in main memory. Finally, the goal of a learning algorithm must be considered. Evaluating the effectiveness of a scaling technique becomes complicated if degradation in the quality of the learning is permitted.

Note that merely generating a random sample from a large database stored on disk may itself be a non-trivial task from a computational viewpoint. , instance representation, selection of interior/boundary points, and instance pruning strategies, have also been investigated in instance-based and nearest neighbor classification frameworks [279]. Challenges in designing an instance selection algorithm include accurate representation of the original data distribution, making fine distinctions at different scales and noticing rare events and anomalies.

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