Results 1 - 10
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314
Clustering of the Self-Organizing Map
, 2000
"... The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quant ..."
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Cited by 280 (1 self)
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The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using-means are investigated. The two-stage procedure---first using SOM to produce the prototypes that are then clustered in the second stage---is found to perform well when compared with direct clustering of the data and to reduce the computation time.
Toward integrating feature selection algorithms for classification and clustering
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals ..."
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Cited by 267 (21 self)
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This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals unattempted combinations, and provides guidelines in selecting feature selection algorithms. With the categorizing framework, we continue our efforts toward building an integrated system for intelligent feature selection. A unifying platform is proposed as an intermediate step. An illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms. An added advantage of doing so is to help a user employ a suitable algorithm without knowing details of each algorithm. Some real-world applications are included to demonstrate the use of feature selection in data mining. We conclude this work by identifying trends and challenges of feature selection research and development.
A survey of evolutionary algorithms for data mining and knowledge discovery
- In: A. Ghosh, and S. Tsutsui (Eds.) Advances in Evolutionary Computation
, 2002
"... Abstract: This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery. We focus on the data mining task of classification. In addition, we discuss some preprocessing and postprocessing steps of the knowled ..."
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Cited by 123 (3 self)
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Abstract: This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery. We focus on the data mining task of classification. In addition, we discuss some preprocessing and postprocessing steps of the knowledge discovery process, focusing on attribute selection and pruning of an ensemble of classifiers. We show how the requirements of data mining and knowledge discovery influence the design of evolutionary algorithms. In particular, we discuss how individual representation, genetic operators and fitness functions have to be adapted for extracting high-level knowledge from data. 1.
KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems ⋆
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Round Robin Classification
, 2002
"... In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifier for each pair of classes. We present an empirical evaluation of the method, implemented as a wrapper around the Ripp ..."
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Cited by 109 (18 self)
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In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifier for each pair of classes. We present an empirical evaluation of the method, implemented as a wrapper around the Ripper rule learning algorithm, on 20 multi-class datasets from the UCI database repository. Our results show that the technique is very likely to improve Ripper's classification accuracy without having a high risk of decreasing it. More importantly, we give a general theoretical analysis of the complexity of the approach and show that its run-time complexity is below that of the commonly used one-against-all technique.
A Framework for the Evaluation of Session Reconstruction Heuristics in Web Usage Analysis
- INFORMS Journal of Computing
"... Web-usage mining has become the subject of intensive research, as its potential forpersonalized services, adaptive Web sites and customer profiling is recognized. How-ever, the reliability of Web-usage mining results depends heavily on the proper preparation of the input datasets. In particular, err ..."
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Cited by 97 (8 self)
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Web-usage mining has become the subject of intensive research, as its potential forpersonalized services, adaptive Web sites and customer profiling is recognized. How-ever, the reliability of Web-usage mining results depends heavily on the proper preparation of the input datasets. In particular, errors in the reconstruction of sessions and incomplete tracing of users ’ activities in a site can easily result in invalid patterns and wrong con-clusions. In this study, we evaluate the performance of heuristics employed to reconstruct sessions from the server log data. Such heuristics are called to partition activities first by user and then by visit of the user in the site, where user identification mechanisms, such as cookies, may or may not be available. We propose a set of performance measures that are sensitive to two types of reconstruction errors and appropriate for different applications in knowledge discovery (KDD) applications. We have tested our framework on the Web server data of a frame-based Web site. The first experiment concerned a specific KDD application and has shown the sensitivity of the heuristics to particularities of the site’s structure and traffic. The second experiment is not bound to a specific application but rather compares the performance of the heuristics for different measures and thus for different application types. Our results show that there is no single best heuristic, but our measures help the analyst in the selection of the heuristic best suited for the application at hand.
Revealing structure within clustered parallel coordinates displays
- IN PROCEEDINGS OF THE 2005 IEEE SYMPOSIUM ON INFORMATION VISUALIZATION
, 2005
"... In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent ..."
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Cited by 68 (5 self)
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In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent
Minority Report in Fraud Detection: Classification of Skewed Data
- ACM SIGKDD EXPLORATIONS
, 2004
"... This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partiti ..."
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Cited by 64 (0 self)
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This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partitions derived from minority oversampling with replacement. Its originality lies in the use of a single meta-classifier (stacking) to choose the best base classifiers, and then combine these base classifiers’ predictions (bagging) to improve cost savings (stacking-bagging). Results from a publicly available automobile insurance fraud detection data set demonstrate that stacking-bagging performs slightly better than the best performing bagged algorithm, C4.5, and its best classifier, C4.5 (2), in terms of cost savings. Stackingbagging also outperforms the common technique used in industry (BP without both sampling and partitioning). Subsequently, this paper compares the new fraud detection method (meta-learning approach) against C4.5 trained using undersampling, oversampling, and SMOTEing without partitioning (sampling approach). Results show that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions. The most interesting find is confirming that the combination of classifiers to produce the best cost savings has its contributions from all three algorithms.
Data Mining
- TO APPEAR IN THE HANDBOOK OF TECHNOLOGY MANAGEMENT, H. BIDGOLI (ED.)
, 2010
"... The amount of data being generated and stored is growing exponentially, due in large part to the continuing advances in computer technology. This presents tremendous opportunities for those who can ..."
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Cited by 48 (1 self)
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The amount of data being generated and stored is growing exponentially, due in large part to the continuing advances in computer technology. This presents tremendous opportunities for those who can
K.: Quality and Complexity Measures for Data Linkage and Deduplication
- Studies in Computational Intelligence (SCI
, 2007
"... Abstract. Deduplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim is to match all records relating to the same entity. Research interest in this area has increased in recent years, with techniques or ..."
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Cited by 47 (15 self)
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Abstract. Deduplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim is to match all records relating to the same entity. Research interest in this area has increased in recent years, with techniques originating from statistics, machine learning, information retrieval, and database research being combined and applied to improve the linkage quality, as well as to increase performance and efficiency when linking or deduplicating very large data sets. Different measures have been used to characterise the quality and complexity of data linkage algorithms, and several new metrics have been proposed. An overview of the issues involved in measuring data linkage and deduplication quality and complexity is presented in this chapter. It is shown that measures in the space of record pair comparisons can produce deceptive accuracy results. Various measures are discussed and recommendations are given on how to assess data linkage and deduplication quality and complexity.
