# Mining All Non-derivable Frequent Itemsets

@article{Calders2002MiningAN, title={Mining All Non-derivable Frequent Itemsets}, author={Toon Calders and Bart Goethals}, journal={ArXiv}, year={2002}, volume={cs.DB/0206004} }

Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently several proposals have been made to construct a concise representation of the frequent itemsets, instead of mining all frequent itemsets. The main goal of this paper is to identify redundancies in the…

## 361 Citations

### Depth-First Non-Derivable Itemset Mining

- Computer ScienceSDM
- 2005

A depth-first algorithm, dfNDI, that is based on Eclat for mining the non-derivable itemsets is presented, and experiments show thatdfNDI outperforms NDI with an order of magnitude.

### Non-derivable itemset mining

- Computer ScienceData Mining and Knowledge Discovery
- 2006

This paper constructs a condensed representation of all frequent itemsets, by removing those itemsets for which the support can be derived, resulting in the so called Non-Derivable Itemsets (NDI) representation.

### Non-Almost-Derivable Frequent Itemsets Mining

- Computer ScienceThe Fifth International Conference on Computer and Information Technology (CIT'05)
- 2005

This paper proposes a new condensed representation called frequent non-almost-derivable itemsets, a subset of the original collection of frequent itemsets that can derive a lower and an upper bound of its support from this representation, and the lower bound and the upper bound is close enough to be controlled by a user-defined parameter.

### A False Negative Maximal Frequent Itemset Mining Algorithm over Stream

- Computer ScienceADMA
- 2011

This paper focuses on mining maximal frequent itemsets approximately over a stream landmark model, and proposes an efficient algorithm named FNMFIMoDS, which achieves a faster speed and a much reduced memory cost in comparison with the state-of-the-art algorithm.

### Efficient Computation of Partial-Support for Mining Interesting Itemsets

- Computer ScienceSDM
- 2009

This paper addresses the problem of efficiently calculating partial supports, which leads to efficient algorithms for mining interesting itemsets in that class, and shows that there exists a recurrence relation between partial supports.

### Dense itemsets

- Computer ScienceKDD
- 2004

This paper addresses the problem of computing all dense itemsets in a database, and gives a levelwise algorithm for this problem, and studies the top-$k$ variations, i.e., finding the k densest sets with a given support, or the k best-supported sets withA given density.

### Deducing Bounds on the Support of Itemsets

- Computer ScienceDatabase Support for Data Mining Applications
- 2004

A complete set of rules for deducing tight bounds on the support of an itemset if the supports of all its subsets are known and how to reduce the size of an adequate representation of the collection of frequent sets is given.

### Finding Top-k Fuzzy Frequent Itemsets from Databases

- Computer ScienceDMBD
- 2017

Theoretical analysis and experimental studies over 4 datasets demonstrate that the proposed algorithm can efficiently decrease the runtime and memory cost, and significantly outperform the naive algorithm Top-k-FFI-Miner.

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