### Abstract

The largest itemset in a given collection of transactions D is the itemset that occurs most frequently in D. This paper studies the problem of finding the N largest itemsets, whose solution can be used to generate an appropriate number of interesting itemsets for mining association rules. We present an efficient algorithm for finding the N largest itemsets. The algorithm is implemented and compared with the naive solution using the Apriori approach. We present experimental results as well as theoretical analysis showing that our algorithm has a much better performance than the naive solution. We also analyze the cost of our algorithm and observe that it has a polynomial time complexity in most cases of practical applications.

Original language | English (US) |
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Title of host publication | Proceedings of the International Conference on Data Mining |

Editors | N.F.F. Ebecken |

Pages | 211-222 |

Number of pages | 12 |

State | Published - 1998 |

Externally published | Yes |

Event | Proceedings of the 1988 International Conference on Data Mining - Rio de Janeiro, Brazil Duration: Sep 2 1998 → Sep 4 1998 |

### Other

Other | Proceedings of the 1988 International Conference on Data Mining |
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City | Rio de Janeiro, Brazil |

Period | 9/2/98 → 9/4/98 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the International Conference on Data Mining*(pp. 211-222)

**Finding the N largest itemsets.** / Shen, Li; Shen, Hong; Pritchard, Paul; Topor, Rodney.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the International Conference on Data Mining.*pp. 211-222, Proceedings of the 1988 International Conference on Data Mining, Rio de Janeiro, Brazil, 9/2/98.

}

TY - GEN

T1 - Finding the N largest itemsets

AU - Shen, Li

AU - Shen, Hong

AU - Pritchard, Paul

AU - Topor, Rodney

PY - 1998

Y1 - 1998

N2 - The largest itemset in a given collection of transactions D is the itemset that occurs most frequently in D. This paper studies the problem of finding the N largest itemsets, whose solution can be used to generate an appropriate number of interesting itemsets for mining association rules. We present an efficient algorithm for finding the N largest itemsets. The algorithm is implemented and compared with the naive solution using the Apriori approach. We present experimental results as well as theoretical analysis showing that our algorithm has a much better performance than the naive solution. We also analyze the cost of our algorithm and observe that it has a polynomial time complexity in most cases of practical applications.

AB - The largest itemset in a given collection of transactions D is the itemset that occurs most frequently in D. This paper studies the problem of finding the N largest itemsets, whose solution can be used to generate an appropriate number of interesting itemsets for mining association rules. We present an efficient algorithm for finding the N largest itemsets. The algorithm is implemented and compared with the naive solution using the Apriori approach. We present experimental results as well as theoretical analysis showing that our algorithm has a much better performance than the naive solution. We also analyze the cost of our algorithm and observe that it has a polynomial time complexity in most cases of practical applications.

UR - http://www.scopus.com/inward/record.url?scp=0032278432&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032278432&partnerID=8YFLogxK

M3 - Conference contribution

SP - 211

EP - 222

BT - Proceedings of the International Conference on Data Mining

A2 - Ebecken, N.F.F.

ER -