Algorithms for clustering data
Algorithms for clustering data
Applied multivariate techniques
Applied multivariate techniques
ACM Computing Surveys (CSUR)
A robust and scalable clustering algorithm for mixed type attributes in large database environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Clustering Algorithms and Validity Measures
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A new cluster validity measure and its application to image compression
Pattern Analysis & Applications
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Moderate diversity for better cluster ensembles
Information Fusion
An experimental comparison of several clustering and initialization methods
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers' consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client's database in order to form the customer's classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer's population than Two-step clustering algorithm.