Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining top-K frequent itemsets from data streams
Data Mining and Knowledge Discovery
Towards a new approach for mining frequent itemsets on data stream
Journal of Intelligent Information Systems
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
Discovering frequent sets from data streams with CPU constraint
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Interactive mining of frequent itemsets over arbitrary time intervals in a data stream
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Mining Multidimensional Sequential Patterns over Data Streams
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
RETRACTED: Efficient mining of temporal emerging itemsets from data streams
Expert Systems with Applications: An International Journal
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Incremental updates of closed frequent itemsets over continuous data streams
Expert Systems with Applications: An International Journal
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Distributed pattern discovery in multiple streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
Parallel and distributed methods for incremental frequent itemset mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining
FINGERPRINT: Summarizing Cluster Evolution in Dynamic Environments
International Journal of Data Warehousing and Mining
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In recent years, new applications emerged that produce data streams, such as stock data and sensor networks. Therefore, finding frequent subsequences, or clusters of subsequences, in data streams is an essential task in data mining. Data streams are continuous in nature, unbounded in size and have a high arrival rate. Due to these characteristics, traditional clustering algorithms fail to effectively find clusters in data streams. Thus, an efficient incremental algorithm is proposed to find frequent subsequences in multiple data streams. The described approach for finding frequent subsequences is by clustering subsequences of a data stream. The proposed algorithm uses a window model to buffer the continuous data streams. Further, it does not recompute the clustering results for the whole data stream at every window, but rather it builds on clustering results of previous windows. The proposed approach also employs a decay value for each discovered cluster to determine when to remove old clusters and retain recent ones. In addition, the proposed algorithm is efficient as it scans the data streams once and it is considered an Any-time algorithm since the frequent subsequences are ready at the end of every window.