Depth first generation of long patterns

By Ramesh C. Agarwal, Charu C. Aggarwal, V. V. V. Prasad. Knowledge Discovery and Data Mining, 2000

396 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/eec78926f02bbadfe25ac1ac86e509fc838efcbc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Charu C. Aggarwal
2. Philip S. Yu
3. Mohammed J. Zaki
4. George Karypis
5. Ching-Jui Hsiao
6. Mukund Deshpande
7. Johannes Gehrke
8. Karam Gouda

In Computer Networks and Communications

1. Mohammed J. Zaki
2. Charu C. Aggarwal
3. Philip S. Yu
4. Mukund Deshpande
5. Karam Gouda
6. Ching-Jui Hsiao
7. Wei Wang
8. George Karypis

In Information Systems

1. Mohammed J. Zaki
2. George Karypis
3. Jiawei Han
4. Ching-Jui Hsiao
5. Karam Gouda
6. John F. Roddick
7. Mukund Deshpande
8. Charu C. Aggarwal

Citations in the corpus

• Item-based top-N recommendation algorithms (2004, methodology): “In particular, using the notion of frequent itemsets [1, 2] developed by the data mining community, we use computationally efficient algorithms [1, 4, 16, 35] to find all frequent itemsets up to size l that occur in σ % of the rows ( i.e ., transactions), and compute the k most similar other items…”
• Efficient Adaptive-Support Association Rule Mining for Recommender Systems (2004, background): “Recent work (Webb, 2000; Agarwal et al., 2000) has begun to address some of these concerns by means of judicious search techniques.”
• A Tree Projection Algorithm for Generation of Frequent Item Sets (2001, background): “A number of algorithms for frequent itemset generation have been proposed [1, 2, 4, 6, 11, 7, 12, 13, 16, 17].”
• Finding recent frequent itemsets adaptively over online data streams (2003, methodology): “monitoring lattice becomes less than a predefined threshold, the itemset is regarded as an insignificant itemset, so that it is pruned from the monitoring lattice as in conventional lattice-based data mining methods [7,8].”
• Fast vertical mining using diffsets (2003, methodology): “DepthProject [2] .nds long itemsets using a depth .rst search of a lexicographic tree of itemsets, and uses a counting method based on transaction pro­jections along its branches.”
• Clustering by pattern similarity in large data sets (2002, background): “The second challenge is that we want to build a “depth-first” [1] clustering algorithm.”
• Association mining (2006, methodology): “In the pursuit of optimizing long itemset discovery, the same authors developed Depth Project [Agrawal et al. 2000].”
• CHARM: An Efficient Algorithm for Closed Itemset Mining (2002, methodology): “The first one is to mine only the maximal frequent itemsets [2, 4, 6, 10, 12], which are typically orders of magnitude fewer than all frequent patterns.”
• Efficiently mining maximal frequent itemsets (2001, methodology): “The real datasets have been used previously in the evaluation of maximal patterns [1, 3, 4].”
• MAFIA: a maximal frequent itemset algorithm (2005, methodology): “6.2.1 DepthProject DepthProject demonstrated an order of magnitude improvement over previous algorithms for mining maximal frequent itemsets [ 38 ].”
• Efficient algorithms for mining closed itemsets and their lattice structure (2005, methodology): “The first one is to mine only the maximal frequent itemsets [2], [4], [6], [10], [13], which are typically orders of magnitude fewer than all frequent patterns.”