KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Counting Distinct Elements in a Data Stream
RANDOM '02 Proceedings of the 6th International Workshop on Randomization and Approximation Techniques
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Finding frequent items in data streams
Theoretical Computer Science - Special issue on automata, languages and programming
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Research issues in data stream association rule mining
ACM SIGMOD Record
A simpler and more efficient deterministic scheme for finding frequent items over sliding windows
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An integrated efficient solution for computing frequent and top-k elements in data streams
ACM Transactions on Database Systems (TODS)
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Finding the frequent items in streams of data
Communications of the ACM - A View of Parallel Computing
Finding Associations and Computing Similarity via Biased Pair Sampling
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Space-optimal heavy hitters with strong error bounds
ACM Transactions on Database Systems (TODS)
Better size estimation for sparse matrix products
APPROX/RANDOM'10 Proceedings of the 13th international conference on Approximation, and 14 the International conference on Randomization, and combinatorial optimization: algorithms and techniques
On Finding Similar Items in a Stream of Transactions
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Frequent Pairs in Data Streams: Exploiting Parallelism and Skew
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
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A straightforward approach to frequent pairs mining in transactional streams is to generate all pairs occurring in transactions and apply a frequent items mining algorithm to the resulting stream. The well-known counter based algorithms Frequent and Space-Saving are known to achieve a very good approximation when the frequencies of the items in the stream adhere to a skewed distribution. Motivated by observations on real datasets, we present a general technique for applying Frequent and Space-Saving to transactional data streams for the case when the transactions considerably vary in their lengths. Despite of its simplicity, we show through extensive experiments that our approach is considerably more efficient and precise than the naïve application of Frequent and Space-Saving.