Database mining: a performance perspective

Agrawal, Rakesh; Imieliński, Tomasz; Swami, Arun

doi:10.1109/69.250074

Cited by 1,194 publications

(499 citation statements)

References 5 publications

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“…To bridge the gap between the linearity in the single layer neural network and the highly complex and computation intensive multi layer neural network, the FLANN architecture is suggested [1] . The FLANN architecture uses a single layer feed forward neural network and to overcome the linear mapping, functionally expands the input vector.…”

Section: Flann Architecturementioning

confidence: 99%

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Functional Link Artificial Neural Network for Classification Task in Data Mining

Misra¹,

Dehuri²

2007

J. of Computer Science

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Abstract:In solving classification task of data mining, the traditional algorithm such as multi-layer perceptron takes longer time to optimize the weight vectors. At the same time, the complexity of the network increases as the number of layers increases. In this study, we have used Functional Link Artificial Neural Networks (FLANN) for the task of classification. In contrast to multiple layer networks, FLANN architecture uses a single layer feed-forward network. Using the functionally expanded features FLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer networks. The features like simplicity of designing the architecture and low-computational complexity of the networks encourages us to use it in data mining task. An extensive simulation study is presented to demonstrate the effectiveness of the classifier.

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Section: Flann Architecturementioning

confidence: 99%

“…For the past few years, there have been many studies [1] focused on the classification task in the emerging field of data mining. In classification, we are given a set of example records, called a training set, where each record consists of several fields or attributes.…”

Section: Introductionmentioning

confidence: 99%

Functional Link Artificial Neural Network for Classification Task in Data Mining

Misra¹,

Dehuri²

2007

J. of Computer Science

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“…The synthetic datasets are generated by the IBM Quest Synthetic Data Generator [12]. We modify their code to generate three extra boolean attributes, using Functions 1-3 described in [1]. Thus, each dataset has six quantitative and six categorical attributes.…”

Section: Datasets and Parametersmentioning

confidence: 99%

An information-theoretic approach to quantitative association rule mining

Cheng

2007

Knowl Inf Syst

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Abstract. Quantitative Association Rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike Boolean Association Rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. However, the combination of these quantitative attributes and their value intervals always gives rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency.In this paper, we propose an information-theoretic approach to avoid unrewarding combinations of both the attributes and their value intervals being generated in the mining process. We study the mutual information between the attributes in a quantitative database and devise a normalization on the mutual information to make it applicable in the context of QAR mining. To indicate the strong informative relationships among the attributes, we construct a mutual information graph (MI graph), whose edges are attribute pairs that have normalized mutual information no less than a predefined information threshold. We find that the cliques in the MI graph represent a majority of the frequent itemsets. We also show that frequent itemsets that do not form a clique in the MI graph are those whose attributes are not informatively correlated to each other. By utilizing the cliques in the MI graph, we devise an efficient algorithm that significantly reduces the number of value intervals of the attribute sets to be joined during the mining process. Extensive experiments show that our algorithm speeds up the mining process by up to two orders of magnitude. Most importantly, we are able to obtain most of the highconfidence QARs, whereas the QARs that are not returned by MIC are shown to be less interesting.

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“…The synthetic data sets were generated using the program of [1]. We generated training data sets with 10,000 to 1,000,000 records, and one testing data set with 5000 records.…”

Section: Empirical Evaluationmentioning

confidence: 99%

Data Mining Using Relational Database Management Systems

Zou

Kemme

et al. 2006

Advances in Knowledge Discovery and Data Mining

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Abstract. Software packages providing a whole set of data mining and machine learning algorithms are attractive because they allow experimentation with many kinds of algorithms in an easy setup. However, these packages are often based on main-memory data structures, limiting the amount of data they can handle. In this paper we use a relational database as secondary storage in order to eliminate this limitation. Unlike existing approaches, which often focus on optimizing a single algorithm to work with a database backend, we propose a general approach, which provides a database interface for several algorithms at once. We have taken a popular machine learning software package, Weka, and added a relational storage manager as back-tier to the system. The extension is transparent to the algorithms implemented in Weka, since it is hidden behind Weka's standard main-memory data structure interface. Furthermore, some general mining tasks are transfered into the database system to speed up execution. We tested the extended system, refered to as WekaDB, and our results show that it achieves a much higher scalability than Weka, while providing the same output and maintaining good computation time.

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Database mining: a performance perspective

Cited by 1,194 publications

References 5 publications

Functional Link Artificial Neural Network for Classification Task in Data Mining

Functional Link Artificial Neural Network for Classification Task in Data Mining

An information-theoretic approach to quantitative association rule mining

Data Mining Using Relational Database Management Systems

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