Data Mining Applications for Empowering Knowledge Societies by Hakikur Rahman

By Hakikur Rahman

Information Mining recommendations are steadily changing into crucial parts of company intelligence platforms and gradually evolving right into a pervasive expertise inside actions that diversity from the usage of historic facts to predicting the good fortune of an understanding crusade. actually, facts mining is turning into an interdisciplinary box pushed through a variety of multi-dimensional applications.
Data Mining purposes for Empowering wisdom Societies offers an outline at the major problems with facts mining, together with its type, regression, clustering, and moral concerns. This accomplished publication additionally presents readers with wisdom bettering procedures in addition to a large spectrum of knowledge mining functions.

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The third step is datasets selection. The training dataset and the testing dataset are selected according to a heuristic process. The fourth step is model formulation and classification. The two-group MCLP and MCQP models are applied to the training dataset to obtain optimal solutions. The solutions are then applied to the testing dataset within which class labels are removed for validation. Based on these scores, each record is predicted as either bad (bankrupt account) or good (current account).

Note that although the boundary of two classes b is the unrestricted variable in Model 4, it can be presumed by the analyst according to the structure of a particular database. First, choosing a proper value of b can speed up solving Model 4. Second, given a thresholdt, the best data separation can be selected from a number of results determined by different b values. Therefore, the parameter b plays a key role in this chapter to achieve and guarantee the desired accuracy ratet. For this reason, the FLP classification method uses b as an important control parameter as shown in Figure 2.

Within each interval, seven records are randomly selected. The number of seven  is determined according to empirical results of k-fold cross-validation. Thus 700 ‘bad’ records are obtained. Second, the good-status dataset (4,185 records) is divided into 100 intervals (each interval has 41 records). Within each interval, seven records are randomly selected. Thus the total of 700 ‘good’ records is obtained. Third, the 700 bankruptcy and 700 current records are combined to form a training dataset.

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