Development Of Data Mining Techniques Research Articles

Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks

The prediction of bank performance is an important issue. The bad performance of banks may first result in bankruptcy, which is expected to influence the economics of the country eventually. Since the early 1970s, many researchers had already made predictions on such issues. However, until recent years, most of them have used traditional statistics to build the prediction model. Because of the vigorous development of data mining techniques, many researchers have begun to apply those techniques to various fields, including performance prediction systems. However, data mining techniques have the problem of parameter settings. Therefore, this study applies particle swarm optimization (PSO) to obtain suitable parameter settings for support vector machine (SVM) and decision tree (DT), and to select a subset of beneficial features, without reducing the classification accuracy rate. In order to evaluate the proposed approaches, dataset collected from Taiwanese commercial banks are used as source data. The experimental results showed that the proposed approaches could obtain a better parameter setting, reduce unnecessary features, and improve the accuracy of classification significantly.

Data Mining in Astronomy

Like other data-rich disciplines such as physics, biology, geology, and oceanography, astronomy is facing a data avalanche due to advances in telescope and detector technology, the exponential increase in computing capabilities, improvements in data-collection methods, and successful applications of theoretical simulations. As the era approaches in which data covers the full range of wavelengths from radio to gamma-rays, the expected data volumes will add up to terabytes, soon to be followed by petabytes. Proper management and processing of massive data sets requires efficient federation of database technologies. However, mining knowledge from huge data volumes is the ultimate goal and development of data-mining techniques is therefore critical. Knowledge discovery in databases (KDD) is the process of extracting useful knowledge from data. Data mining, the application of specific algorithms to discover rare or previously unknown types of object or phenomenon, is a particular step in the process.1 KDD is inherently interactive and iterative, as shown in Figure 1. Common KDD functions are classification, cluster analysis, and regression. In classification one develops a description or model for each class of data labeled with discrete integers (as opposed to cluster analysis, which is sometimes called ‘unsupervised classification’). Classification is used for the organization of future test data, better understanding of each data class, and predictions of certain properties and behaviors. It is based on spectra or images and, for example, may be used to describe galaxies by morphology. Cluster (or clustering) analysis is a multivariate procedure based on placing objects into more or less homogeneous groups such that the relationship between groups is revealed. It lacks an underlying body of statistical theory and is heuristic in nature, requiring decisions to be made by individual users (which can strongly affect results). Cluster analysis is used to classify groups or objectsmore objectively than subjectively and can help astronomers find unusual objects within a flood of data. ExamFigure 1. Knowledge discovery in databases.

Privacy preserving clustering on horizontally partitioned data

Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. However, the popularity and wide availability of data mining tools also raised concerns about the privacy of individuals. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be applied on databases without violating the privacy of individuals. Privacy preserving techniques for various data mining models have been proposed, initially for classification on centralized data then for association rules in distributed environments. In this work, we propose methods for constructing the dissimilarity matrix of objects from different sites in a privacy preserving manner which can be used for privacy preserving clustering as well as database joins, record linkage and other operations that require pair-wise comparison of individual private data objects horizontally distributed to multiple sites. We show communication and computation complexity of our protocol by conducting experiments over synthetically generated and real datasets. Each experiment is also performed for a baseline protocol, which has no privacy concern to show that the overhead comes with security and privacy by comparing the baseline protocol and our protocol.

STRUCTURE DISCOVERY IN SEQUENTIALLY-CONNECTED DATA STREAMS

Historically, data mining research has been focused on discovering sets of attributes that discriminate data entities into classes or association rules between attributes. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event driven, such as counter-terrorism intelligence analysis. In this paper we describe an algorithm designed to operate over relational data received from a continuous stream. Our approach includes a mechanism for summarizing discoveries from previous data increments so that the globally best patterns can be computed by examining only the new data increment. We then describe a method by which relational dependencies that span across temporal increment boundaries can be efficiently resolved so that additional pattern instances, which do not reside entirely in a single data increment, can be discovered. We also describe a method for change detection using a measure of central tendency designed for graph data. We contrast two formulations of the change detection process and demonstrate the ability to identify salient changes along meaningful dimensions and recognize trends in a relational data stream.

ITERATIVE STRUCTURE DISCOVERY IN GRAPH-BASED DATA

Much of current data mining research is focused on discovering sets of attributes that discriminate data entities into classes, such as shopping trends for a particular demographic group. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event-driven or relationally structured. In this paper we present approaches to address two related challenges; the need to assimilate incremental data updates and the need to mine monolithic datasets. Many realistic problems are continuous in nature and therefore require a data mining approach that can evolve discovered knowledge over time. Similarly, many problems present data sets that are too large to fit into dynamic memory on conventional computer systems. We address incremental data mining by introducing a mechanism for summarizing discoveries from previous data increments so that the globally-best patterns can be computed by mining only the new data increment. To address monolithic datasets we introduce a technique by which these datasets can be partitioned and mined serially with minimal impact on the result quality. We present applications of our work in both the counter-terrorism and bioinformatics domains.

Mining residue contacts in proteins using local structure predictions

In this paper we develop data mining techniques to predict 3D contact potentials among protein residues (or amino acids) based on the hierarchical nucleation-propagation model of protein folding. We apply a hybrid approach, using a hidden Markov model to extract folding initiation sites, and then apply association mining to discover contact potentials. The new hybrid approach achieves accuracy results better than those reported previously.

Data mining for Web intelligence

Searching, comprehending, and using the semistructured HTML, XML, and database-service-engine information stored on the Web poses a significant challenge. This data is more sophisticated and dynamic than the information commercial database systems store. To supplement keyword-based indexing, researchers have applied data mining to Web-page ranking. In this context, data mining helps Web search engines find high-quality Web pages and enhances Web click stream analysis. For the Web to reach its full potential, however, we must improve its services, make it more comprehensible, and increase its usability. As researchers continue to develop data mining techniques, the authors believe this technology will play an increasingly important role in meeting the challenges of developing the intelligent Web. Ultimately, data mining for Web intelligence will make the Web a richer, friendlier, and more intelligent resource that we can all share and explore. The paper considers how data mining holds the key to uncovering and cataloging the authoritative links, traversal patterns, and semantic structures that will bring intelligence and direction to our Web interactions.

Mining fuzzy association rules for classification problems

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems.