A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Mapping context dependent acoustic information into context independent form by LVQ
Speech Communication
Self-organizing map network as an interactive clustering tool—an application to group technology
Decision Support Systems - Special issue on WITS '92
Data mining and knowledge discovery in databases
Communications of the ACM
Self-organizing maps
Data mining: a hands-on approach for business professionals
Data mining: a hands-on approach for business professionals
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
ACM Computing Surveys (CSUR)
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Pattern recognition in time series database: A case study on financial database
Expert Systems with Applications: An International Journal
Incremental clustering of mixed data based on distance hierarchy
Expert Systems with Applications: An International Journal
Modified adaptive resonance theory network for mixed data based on distance hierarchy
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Clustering of heterogeneously typed data with soft computing - a case study
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
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Data mining uncovers hidden, previously unknown, and potentially useful information from large amounts of data. Compared to the traditional statistical and machine learning data analysis techniques, data mining emphasizes providing a convenient and complete environment for the data analysis. In this paper, we propose an integrated framework for visualized, exploratory data clustering, and pattern extraction from mixed data. We further discuss its implementation techniques: a generalized self-organizing map (GSOM) and an extended attribute-oriented induction (EAOI), which not only overcome the drawbacks of their original algorithms, but also provide additional analysis capabilities. Specifically, the GSOM facilitates the direct handling of mixed data, including categorical and numeric values. The EAOI enables exploration for major values hidden in the data and, in addition, offers an alternative for processing numeric attributes, instead of generalizing them. A prototype was developed for experiments with synthetic and real data sets, and comparison with those of the traditional approaches. The results confirmed the feasibility of the framework and the superiority of the extended techniques.