Algorithms for clustering data
Algorithms for clustering data
Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Fast discovery of association rules
Advances in knowledge discovery and data mining
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
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Selection of Views to Materialize in a Data Warehouse
ICDT '97 Proceedings of the 6th International Conference on Database Theory
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd 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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Knowledge Discovery in Spatial Databases
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Data Organization and Access for Efficient Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining data streams under block evolution
ACM SIGKDD Explorations Newsletter
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Discovering decision rules from numerical data streams
Proceedings of the 2004 ACM symposium on Applied computing
Clustering in Dynamic Spatial Databases
Journal of Intelligent Information Systems
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
Perfect hashing schemes for mining traversal patterns
Fundamenta Informaticae
XML structural delta mining: issues and challenges
Data & Knowledge Engineering - Special issue: ER 2003
Dynamic Association Rule Mining using Genetic Algorithms
Intelligent Data Analysis
A Data-Mining-Based Prefetching Approach to Caching for Network Storage Systems
INFORMS Journal on Computing
Using multiple windows to track concept drift
Intelligent Data Analysis
Clustering over Multiple Evolving Streams by Events and Correlations
IEEE Transactions on Knowledge and Data Engineering
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Collaborative filtering on streaming data with interest-drifting
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Data mining research for customer relationship management systems: a framework and analysis
International Journal of Business Information Systems
ACM SIGKDD Explorations Newsletter
Supporting Customer Retention through Real-Time Monitoring of Individual Web Usage
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Incremental clustering of dynamic data streams using connectivity based representative points
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
DRFP-tree: disk-resident frequent pattern tree
Applied Intelligence
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
HE-Tree: a framework for detecting changes in clustering structure for categorical data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Towards subspace clustering on dynamic data: an incremental version of PreDeCon
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Density based subspace clustering over dynamic data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
An efficient itemset mining approach for data streams
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
COMET: event-driven clustering over multiple evolving streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Constructing complete FP-Tree for incremental mining of frequent patterns in dynamic databases
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Clustering large dynamic datasets using exemplar points
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Distribution based data filtering for financial time series forecasting
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Perfect Hashing Schemes for Mining Traversal Patterns
Fundamenta Informaticae
Exploiting online social data in ontology learning for event tracking and emergency response
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Data mining algorithms have been the focus of much research recently. In practice, the input data to a data mining process resides in a large data warehouse whose data is kept up-to-date through periodic or occasional addition and deletion of blocks of data. Most data mining algorithms have either assumed that the input data is static, or have been designed for arbitrary insertions and deletions of data records. In this paper, we consider a dynamic environment that evolves through systematic addition or deletion of blocks of data. We introduce a new dimension, called the data span dimension, which allows user-defined selections of a temporal subset of the database. Taking this new degree of freedom into account, we describe efficient model maintenance algorithms for frequent itemsets and clusters. We then describe a generic algorithm that takes any traditional incremental model maintenance algorithm and transforms it into an algorithm that allows restrictions on the data span dimension. We also develop an algorithm for automatically discovering a specific class of interesting block selection sequences. In a detailed experimental study, we examine the validity and performance of our ideas on synthetic and real datasets.