Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A comparative study of clustering methods
Future Generation Computer Systems - Special double issue on data mining
Snakes and sandwiches: optimal clustering strategies for a data warehouse
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficiently clustering transactional data with weighted coverage density
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Mining association rules in very large clustered domains
Information Systems
Association mining in time-varying domains
Intelligent Data Analysis
Finding regional co-location patterns for sets of continuous variables in spatial datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Determining the best K for clustering transactional datasets: A coverage density-based approach
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
“Best K”: critical clustering structures in categorical datasets
Knowledge and Information Systems
Data Mining and Knowledge Discovery
Mining High-Correlation Association Rules for Inferring Gene Regulation Networks
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
SCALE: a scalable framework for efficiently clustering transactional data
Data Mining and Knowledge Discovery
Evolutionary approach for mining association rules on dynamic databases
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
A neighborhood graph based approach to regional co-location pattern discovery: a summary of results
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mining and validation of localized frequent web access patterns with dynamic tolerance
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Clustering categorical data using coverage density
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Expert Systems with Applications: An International Journal
A cluster centers initialization method for clustering categorical data
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
A novel fuzzy clustering algorithm with between-cluster information for categorical data
Fuzzy Sets and Systems
| Hi-index | 0.01 |
In this paper, we discuss a technique for discovering localized associations in segments of the data using clustering. Often, the aggregate behavior of a data set may be very different from localized segments. In such cases, it is desirable to design algorithms which are effective in discovering localized associations because they expose a customer pattern which is more specific than the aggregate behavior. This information may be very useful for target marketing. We present empirical results which show that the method is indeed able to find a significantly larger number of associations than what can be discovered by analysis of the aggregate data.