Intelligent Data Analysis: An International Journal

Mining sequential patterns from temporal transaction databases attempts to find customer behavior models and to assist managers in making correct and effective decisions. The sequential patterns discovered may, however, become invalid or inappropriate when databases are updated. Conventional approaches may re-mine entire databases to get correct sequential patterns for maintenance. However, when a database is massive in size, this will require considerable computation time. In the past, Lin and Lee proposed an incremental mining algorithm for maintenance of sequential patterns as new records were inserted. In addition to record insertion, record deletion is also commonly seen in real-world applications. Processing record deletion is, however, different from processing record insertion. The former can even be thought of the contrary of the latter. In this paper, we thus attempt to design an effective maintenance algorithm for sequential patterns as records are deleted. Our proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus reducing numbers of rescanning databases. In addition, rescanning requirement depends on decreased numbers of customers, which are usually zero when numbers of deleted records are not large. This characteristic is especially useful for dynamic database mining.

We developed computational and theoretical methods to analyze the nature of experimental data. Our objective was to reveal how the protein secondary structure types behave in a space defined by a sequence of a certain length. Structure α-helix was only slightly more compact than the β-strand. The mean distance within the PPII structure class was the smallest, but the structure was not as compact as the others. This could be a consequence of the distance metric applied and the sensitivity of the structure to proline. In addition, this work describes some mathematical properties of the sequence space which explains the behaviour of secondary structure types in the space. This work gives an account of how prediction accuracy for conventional local prediction methods can be understood and explains why local prediction is so difficult.

In machine learning problems, differences in prior class probabilities -- or class imbalances -- have been reported to hinder the performance of some standard classifiers, such as decision trees. This paper presents a systematic study aimed at answering three different questions. First, we attempt to understand the nature of the class imbalance problem by establishing a relationship between concept complexity, size of the training set and class imbalance level. Second, we discuss several basic re-sampling or cost-modifying methods previously proposed to deal with the class imbalance problem and compare their effectiveness. The results obtained by such methods on artificial domains are linked to results in real-world domains. Finally, we investigate the assumption that the class imbalance problem does not only affect decision tree systems but also affects other classification systems such as Neural Networks and Support Vector Machines.

Identification of outliers can lead to the discovery of unexpected and interesting knowledge. Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We define statistical tests, analyze the statistical foundation underlying our approach, design a fast algorithm to detect spatial outliers, and provide cost models for outlier detection procedures. In addition, we provide experimental results from the application of our algorithm on a Minneapolis-St. Paul (Twin Cities) traffic data set to show its effectiveness and usefulness.

Most e-commerce web sites are very large, often confusing and overwhelm the visitor with a huge amount of information. People cannot easily find what they are looking for. Moreover, the web site is presented in the same format to every visitor, irrespective of his needs. This paper proposes a model to solve the above problems. Our model divides the visitors into groups. Then it arranges web pages in the decreasing order of preference for each group and applies path prediction to find sink pages for that group. The results of these algorithms are displayed in a separate frame without modifying the site. Secondly, our paper addresses the need to guide visitors during the configuration of a product. We propose to apply data mining on the quotes for every group and find associations between different components of the product. We can then display these results as suggestions while the prospective buyer is configuring the product. These suggestions will dynamically change as each selection is made. We have discussed the data mining algorithms applicable to our model.