Mining Association Rules From Market Basket Data Using Share Measures and Characterized Itemsets

Hilderman, Robert J.; Hamilton, Howard J.; Carter, Colin L.; Cercone, Nick

doi:10.1142/s0218213098000111

Cited by 21 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Augmenting standard association rules has previously been proposed in [6]. This approach applies attributeoriented generalization algorithm to characteristic attributes of discovered association rules.…”

Section: Extended Association Rulesmentioning

confidence: 99%

“…This approach applies attributeoriented generalization algorithm to characteristic attributes of discovered association rules. Our approach differs from [6] in that we involve all attributes (not just items) in finding the frequent itemsets. Thus, there are more opportunities for optimizations (see Section 5.2) and the discovered rules have finer-granularity.…”

Section: Extended Association Rulesmentioning

confidence: 99%

See 1 more Smart Citation

Ad-hoc association-rule mining within the data warehouse

Nestorov

Jukić

2003

36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of The

View full text Add to dashboard Cite

Many organizations often underutilize their already constructed data warehouses. In this paper, we suggest a novel way of acquiring more information from corporate data warehouses without the complications and drawbacks of deploying additional software systems. Association-rule mining, which captures co-occurrence patterns within data, has attracted considerable efforts from data warehousing researchers and practitioners alike. Unfortunately, most data mining tools are loosely coupled, at best, with the data warehouse repository. Furthermore, these tools can often find association rules only within the main fact table of the data warehouse (thus ignoring the information-rich dimensions of the star schema) and are not easily applied on non-transaction level data often found in data warehouses. In this paper, we present a new data-mining framework that is tightly integrated with the data warehousing technology. Our framework has several advantages over the use of separate data mining tools. First, the data stays at the data warehouse, and thus the management of security and privacy issues is greatly reduced. Second, we utilize the query processing power of a data warehouse itself, without using a separate data-mining tool. In addition, this framework allows ad-hoc data mining queries over the whole data warehouse, not just over a transformed portion of the data that is required when a standard datamining tool is used. Finally, this framework also expands the domain of association-rule mining from transactionlevel data to aggregated data as well.

show abstract

Section: Extended Association Rulesmentioning

confidence: 99%

Section: Extended Association Rulesmentioning

confidence: 99%

Ad-hoc association-rule mining within the data warehouse

Nestorov

Jukić

2003

36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of The

View full text Add to dashboard Cite

show abstract

“…Share can be incorporated into many algorithms developed for support [5]. Approaches have been proposed for extending support to quantitative measures, e.g.…”

Section: Review Of the Share Measurementioning

confidence: 99%

Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets

Barber

Hamilton

2000

Principles of Data Mining and Knowledge Discovery

Self Cite

View full text Add to dashboard Cite

The share measure for itemsets provides useful information about numerical values associated with transaction items, that the support measure cannot. Finding share frequent itemsets is difficult because share frequency is not downward closed when it is defined in terms of the itemset as a whole. The Item Add-back and Combine All Counted algorithms do not rely on downward closure and thus, are able to find share frequent itemsets that have infrequent subsets. These heuristic algorithms predict which itemsets should be counted in the current pass using information available at no additional processing cost.

show abstract

“…Firstly, these schemes still consider the support of an itemset to measure their importance and secondly, these models do not employ the quantities or prices of items purchased. Several researchers have also proposed itemset share measure, which is the fraction of some numerical value in order to overcome these shortcomings [5,6,8,17,18,19,24,25]. Carter et al [8] proposed a share-confidence model to discover association rule among numerical attributes which are associated with items in a transaction.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Mining High Utility Closed Itemsets and Generators

Sahoo¹,

Das²,

Goswami³

2014

Preprint

View full text Add to dashboard Cite

Traditional association rule mining based on the support-confidence framework provides the objective measure of the rules that are of interest to users. However, it does not reflect the utility of the rules. To extract non-redundant association rules in support-confidence framework frequent closed itemsets and their generators play an important role. To extract non-redundant association rules among high utility itemsets, high utility closed itemsets (HUCI) and their generators should be extracted in order to apply traditional support-confidence framework. However, no efficient method exists at present for mining HUCIs with their generators. This paper addresses this issue. A postprocessing algorithm, called the HUCI-Miner, is proposed to mine HUCIs with their generators. The proposed algorithm is implemented using both synthetic and real datasets.

show abstract

Mining Association Rules From Market Basket Data Using Share Measures and Characterized Itemsets

Cited by 21 publications

References 19 publications

Ad-hoc association-rule mining within the data warehouse

Ad-hoc association-rule mining within the data warehouse

Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets

An Algorithm for Mining High Utility Closed Itemsets and Generators

Contact Info

Product

Resources

About