Synopsis data structures for massive data sets
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Continuous queries over data streams
ACM SIGMOD Record
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Issues in data stream management
ACM SIGMOD Record
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Sequential Pattern Mining in Multiple Streams
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Research issues in data stream association rule mining
ACM SIGMOD Record
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
Incremental Mining of Sequential Patterns over a Stream Sliding Window
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
Streaming data reduction using low-memory factored representations
Information Sciences: an International Journal
A scalable supervised algorithm for dimensionality reduction on streaming data
Information Sciences: an International Journal
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
WSFI-Mine: Mining Frequent Patterns in Data Streams
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
An improved frequent pattern growth method for mining association rules
Expert Systems with Applications: An International Journal
MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams
Journal of Information Science
Mining regular patterns in data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
A dynamic layout of sliding window for frequent itemset mining over data streams
Journal of Systems and Software
Positive and negative association rule mining on XML data streams in database as a service concept
Expert Systems with Applications: An International Journal
Mining frequent patterns from dynamic data streams with data load management
Journal of Systems and Software
Towards a variable size sliding window model for frequent itemset mining over data streams
Computers and Industrial Engineering
A sliding window-based false-negative approach for ubiquitous data stream analysis
International Journal of Communication Systems
Computers & Mathematics with Applications
Rare pattern mining on data streams
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams
ACM Transactions on Management Information Systems (TMIS)
A sliding window based algorithm for frequent closed itemset mining over data streams
Journal of Systems and Software
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
An adaptive ensemble classifier for mining concept drifting data streams
Expert Systems with Applications: An International Journal
Mining associated sensor patterns for data stream of wireless sensor networks
Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Identifying streaming frequent items in ad hoc time windows
Data & Knowledge Engineering
Associative Classification based Human Activity Recognition and Fall Detection using Accelerometer
International Journal of Intelligent Information Technologies
Stream mining on univariate uncertain data
Applied Intelligence
Mining frequent itemsets in a stream
Information Systems
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
Mining frequent itemsets in data streams within a time horizon
Data & Knowledge Engineering
UT-Tree: Efficient mining of high utility itemsets from data streams
Intelligent Data Analysis
Hi-index | 12.07 |
Online mining of frequent itemsets over a stream sliding window is one of the most important problems in stream data mining with broad applications. It is also a difficult issue since the streaming data possess some challenging characteristics, such as unknown or unbound size, possibly a very fast arrival rate, inability to backtrack over previously arrived transactions, and a lack of system control over the order in which the data arrive. In this paper, we propose an effective bit-sequence based, one-pass algorithm, called MFI-TransSW (Mining Frequent Itemsets within a Transaction-sensitive Sliding Window), to mine the set of frequent itemsets from data streams within a transaction-sensitive sliding window which consists of a fixed number of transactions. The proposed MFI-TransSW algorithm consists of three phases: window initialization, window sliding and pattern generation. First, every item of each transaction is encoded in an effective bit-sequence representation in the window initialization phase. The proposed bit-sequence representation of item is used to reduce the time and memory needed to slide the windows in the following phases. Second, MFI-TransSW uses the left bit-shift technique to slide the windows efficiently in the window sliding phase. Finally, the complete set of frequent itemsets within the current sliding window is generated by a level-wise method in the pattern generation phase. Experimental studies show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than do existing algorithms for mining frequent itemsets over data streams with a sliding window. Furthermore, based on the MFI-TransSW framework, an extended single-pass algorithm, called MFI-TimeSW (Mining Frequent Itemsets within a Time-sensitive Sliding Window) is presented to mine the set of frequent itemsets efficiently over time-sensitive sliding windows.