ABSTRACT
We address the problem of mining maximally informative k-itemsets (miki) in data streams based on joint entropy. We propose PentroS, a highly scalable parallel miki mining algorithm. PentroS renders the mining process of large volumes of incoming data very efficient. It is designed to take into account the continuous aspect of data streams, particularly by reducing the computations of need for updating the miki results after arrival/departure of transactions to/from the sliding window. PentroS has been extensively evaluated using massive real-world data streams. Our experimental results confirm the effectiveness of our proposal which allows excellent throughput with high itemset length.
- Youssef Bassil. 2012. A Survey on Information Retrieval, Text Categorization, and Web Crawling. CoRR (2012).Google Scholar
- Moses Charikar, Kevin C. Chen, and Martin Farach-Colton. 2004. Finding frequent items in data streams. Theor. Comput. Sci. (2004). Google ScholarDigital Library
- Thomas M. Cover and Joy A. Thomas. 2006. Elements of information theory (2. ed.). Wiley.Google ScholarDigital Library
- Jeffrey Dean and Sanjay Ghemawat. 2004. MapReduce: Simplified Data Processing on Large Clusters. In OSDI 2004. San Francisco, California, USA. Google ScholarDigital Library
- Anna C. Gilbert, Yannis Kotidis, S. Muthukrishnan, and Martin Strauss. 2001. Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries. In VLDB 2001. Roma, Italy. Google ScholarDigital Library
- Florin Gorunescu. 2011. Data Mining - Concepts, Models and Techniques. Springer.Google Scholar
- Hannes Heikinheimo, Jouni K. Seppänen, Eino Hinkkanen, Heikki Mannila, and Taneli Mielikäinen. 2007. Finding low-entropy sets and trees from binary data. In ACM SIGKDD 2007. San Jose, California, USA. Google ScholarDigital Library
- Cong-Rui Ji and Zhi-Hong Deng. 2007. Mining Frequent Ordered Patterns without Candidate Generation. In FSKD 2007. Haikou, Hainan, China. Google ScholarDigital Library
- Arno J. Knobbe and Eric K. Y. Ho. 2006. Maximally informative k-itemsets and their efficient discovery. In ACM SIGKDD 2006. Philadelphia, PA, USA. Google ScholarDigital Library
- Hoang Thanh Lam and Toon Calders. 2010. Mining top-k frequent items in a data stream with flexible sliding windows. In ACM SIGKDD 2010. Washington, DC, USA. Google ScholarDigital Library
- Sandy Moens, Emin Aksehirli, and Bart Goethals. 2013. Frequent Itemset Mining for Big Data. In IEEE BigData 2013. Santa Clara, CA, USA.Google ScholarCross Ref
- Odysseas Papapetrou, Minos N. Garofalakis, and Antonios Deligiannakis. 2015. Sketching distributed sliding-window data streams. The VLDB Journal (2015). Google ScholarDigital Library
- Thomas A. Runkler. 2016. Data Analytics - Models and Algorithms for Intelligent Data Analysis. Springer. Google ScholarDigital Library
- Saber Salah, Reza Akbarinia, and Florent Masseglia. 2015. Fast Parallel Mining of Maximally Informative k-Itemsets in Big Data. In ICDM 2015. Atlantic City, USA. Google ScholarDigital Library
- Wei-Guang Teng, Ming-Syan Chen, and Philip S. Yu. 2003. A Regression-Based Temporal Pattern Mining Scheme for Data Streams. In VLDB 2003. Google ScholarDigital Library
- Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster Computing with Working Sets. In HotCloud 2010. Boston, USA. Google ScholarDigital Library
- Chongsheng Zhang and Florent Masseglia. 2010. Discovering Highly Informative Feature Sets from Data Streams. In DEXA 2010. Bilbao, Spain. Google ScholarDigital Library
- Mehdi Zitouni, Reza Akbarinia, Sadok Ben Yahia, and Florent Masseglia. 2015. A Prime Number Based Approach for Closed Frequent Itemset Mining in Big Data. In DEXA 2015. Valencia, Spain. Google ScholarDigital Library
Index Terms
- Maximally informative k-itemset mining from massively distributed data streams
Recommendations
Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis
Mining frequent itemsets over data streams has attracted much research attention in recent years. In the past, we had developed a hash-based approach for mining frequent itemsets over a single data stream. In this paper, we extend that approach to mine ...
SWEclat: a frequent itemset mining algorithm over streaming data using Spark Streaming
AbstractFinding frequent itemsets in a continuous streaming data is an important data mining task which is widely used in network monitoring, Internet of Things data analysis and so on. In the era of big data, it is necessary to develop a distributed ...
Frequent Closed Informative Itemset Mining
CIS '07: Proceedings of the 2007 International Conference on Computational Intelligence and SecurityIn recent years, cluster analysis and association analysis have attracted a lot of attention for large data analysis such as biomedical data analysis. This paper proposes a novel algorithm of frequent closed itemset mining. The algorithm addresses two ...
Comments