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Adherence clustering: an efficient method for mining market-basket clusters
- Source :
- Information Systems. 31:170-186
- Publication Year :
- 2006
- Publisher :
- Elsevier BV, 2006.
-
Abstract
- We explore in this paper the efficient clustering of market-basket data. Different from those of the traditional data, the features of market-basket data are known to be of high dimensionality and sparsity. Without explicitly considering the presence of the taxonomy, most prior efforts on clustering market-basket data can be viewed as dealing with items in the leaf level of the taxonomy tree. Clustering transactions across different levels of the taxonomy is of great importance for marketing strategies as well as for the result representation of the clustering techniques for market-basket data. In view of the features of market-basket data, we devise in this paper a novel measurement, called the category-based adherence, and utilize this measurement to perform the clustering. With this category-based adherence measurement, we develop an efficient clustering algorithm, called algorithm k-todes, for market-basket data with the objective to minimize the category-based adherence. The distance of an item to a given cluster is defined as the number of links between this item and its nearest tode. The category-based adherence of a transaction to a cluster is then defined as the average distance of the items in this transaction to that cluster. A validation model based on information gain is also devised to assess the quality of clustering for market-basket data. As validated by both real and synthetic datasets, it is shown by our experimental results, with the taxonomy information, algorithm k-todes devised in this paper significantly outperforms the prior works in both the execution efficiency and the clustering quality as measured by information gain, indicating the usefulness of category-based adherence in market-basket data clustering.
- Subjects :
- Clustering high-dimensional data
DBSCAN
Fuzzy clustering
Computer science
Single-linkage clustering
Correlation clustering
Conceptual clustering
computer.software_genre
Biclustering
CURE data clustering algorithm
Consensus clustering
Cluster analysis
k-medians clustering
Brown clustering
Constrained clustering
Hierarchical clustering
Determining the number of clusters in a data set
Data stream clustering
Hardware and Architecture
Canopy clustering algorithm
FLAME clustering
Affinity propagation
Data mining
computer
Software
Information Systems
Subjects
Details
- ISSN :
- 03064379
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- Information Systems
- Accession number :
- edsair.doi...........243f803c970a0ad62f373fd0da6c0227