Algorithms for clustering data
Algorithms for clustering data
Automatic text structuring and retrieval-experiments in automatic encyclopedia searching
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Incremental clustering for dynamic information processing
ACM Transactions on Information Systems (TOIS)
ACM Computing Surveys (CSUR)
Incremental clustering for profile maintenance in information gathering web agents
Proceedings of the fifth international conference on Autonomous agents
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
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
An Integrated Framework for Visualized and Exploratory Pattern Discovery in Mixed Data
IEEE Transactions on Knowledge and Data Engineering
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Generalizing self-organizing map for categorical data
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data
Knowledge-Based Systems
Adjusting the clustering results referencing an external set
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Dynamic clustering with soft computing
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A data mining approach to knowledge discovery from multidimensional cube structures
Knowledge-Based Systems
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Clustering is an important function in data mining. Its typical application includes the analysis of consumer's materials. Adaptive resonance theory network (ART) is very popular in the unsupervised neural network. Type I adaptive resonance theory network (ART1) deals with the binary numerical data, whereas type II adaptive resonance theory network (ART2) deals with the general numerical data. Several information systems collect the mixing type attitudes, which included numeric attributes and categorical attributes. However, ART1 and ART2 do not deal with mixed data. If the categorical data attributes are transferred to the binary data format, the binary data do not reflect the similar degree. It influences the clustering quality. Therefore, this paper proposes a modified adaptive resonance theory network (M-ART) and the conceptual hierarchy tree to solve similar degrees of mixed data. This paper utilizes artificial simulation materials and collects a piece of actual data about the family income to do experiments. The results show that the M-ART algorithm can process the mixed data and has a great effect on clustering.