A Confidence-Lift Support Specification for Interesting Associations Mining

Lin, Wen-Yang; Tseng, Ming-Cheng; Su, Ja-Hwung

doi:10.1007/3-540-47887-6_14

Cited by 20 publications

(18 citation statements)

References 9 publications

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“…In [18], Liu et al thus proposed an algorithm that using multiple minimum supports, called Minimum Item Supports (MISs), for mining association rules. Lots of approaches have then been proposed approach for setting appropriate multiple minimum supports by using heuristics methods [15,19,27].…”

Section: B the Main Issue Of The Minimum Support Thresholdmentioning

confidence: 99%

Mining fuzzy coherent rules from quantitative transactions without minimum support threshold

Chen

Lee

et al. 2012

2012 IEEE International Conference on Fuzzy Systems

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Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested in business point of view. In this paper, we thus proposed an algorithm for mining fuzzy coherent rules to overcome those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.

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Section: B the Main Issue Of The Minimum Support Thresholdmentioning

confidence: 99%

Mining fuzzy coherent rules from quantitative transactions without minimum support threshold

Chen

Lee

et al. 2012

2012 IEEE International Conference on Fuzzy Systems

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“…Lin et al [12] devised an approach for finding the minimum support threshold for each item. This approach does not need a preset minimum support threshold but a parameter that determines the actual discovery of frequent item sets [13].…”

Section: Loss Of Association Rules Involving Infrequently Observed Itemsmentioning

confidence: 99%

“…The research by Liu et al [7], Lin et al [12], Yun et al [13], and Koh et al [9] has been important in establishing a minimum item support threshold with finer granularity although different criteria were injected for identifying minimum item support values. The common aim, however, was to offset heuristics when setting up a minimum support threshold.…”

Section: Loss Of Association Rules Involving Infrequently Observed Itemsmentioning

confidence: 99%

“…In all these approaches, we see that state-of-the-art association rule mining has drifted from the original idea of mining frequent patterns alone to considering other patterns as well. These include patterns above a parameter that signifies how items should be valued [7], patterns that are not negatively correlated [12], patterns that are infrequent but with a relatively high occurrences [13], and patterns that are above coincidences [9]. Using a minimum support threshold alone cannot identify these patterns specifically.…”

Section: Loss Of Association Rules Involving Infrequently Observed Itemsmentioning

confidence: 99%

“…Antonie and Zaïane [23] used Pearson's correlation coefficient to search for both positive and negative association rules that have a strong correlation. (By contrast, Lin et al [12] remove the negative association rules.) The search algorithm [23] found the strongest correlated rules, followed by rules with moderate and small strength values.…”

Section: Association Rules That Are Measured Using Other Measures Of mentioning

confidence: 99%

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Logic-Based Pattern Discovery

Sim

Indrawan

Zutshi

et al. 2010

IEEE Trans. Knowl. Data Eng.

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In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Often, the number of association rules, even though large in number, misses some interesting rules and the rules' quality necessitates further analysis. As a result, decision making using these rules could lead to risky actions. We propose a framework to discover domain knowledge report as coherent rules. Coherent rules are discovered based on the properties of propositional logic, and therefore, requires no background knowledge to generate them. From the coherent rules discovered, association rules can be derived objectively and directly without knowing the level of minimum support threshold required. We provide analysis of the rules compare to those discovered via the a priori.

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An EEG‐based neuro‐recommendation system for improving consumer purchase experience

Panda

Chakladar

Rana

et al. 2023

J of Consumer Behaviour

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The aperture between the marketing domain and the electroencephalography (EEG)‐based brain–computer interface (BCI) has been achieved with the inception of neuromarketing. This domain helps access the hidden information of the preferences and tastes of the consumers who intend to purchase. Research scholars have experimented with this emerging area in multiple aspects like designing pricing, promotions, predicting purchase‐related activities, new product development, and so on. In this study, we have proposed an innovative use of neuromarketing to build a recommendation system. This recommendation system can potentially suggest suitable products to the consumer based on the past purchase behavior. This proposal carries huge potential in converting visitors to shoppers, increasing average order value, increasing the number of items per order, designing personalized promotions, and so on. The commonality of activated brain signals has been used to build this recommendation system. This neuromarketing‐based recommendation system carries the advantage over the traditional recommendation system as this system suggests products based on the actual real‐time state of the brain during the purchase. This system successfully initiated the starting point of building a neuromarketing‐based recommendation system.

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A Confidence-Lift Support Specification for Interesting Associations Mining

Cited by 20 publications

References 9 publications

Mining fuzzy coherent rules from quantitative transactions without minimum support threshold

Mining fuzzy coherent rules from quantitative transactions without minimum support threshold

Logic-Based Pattern Discovery

An EEG‐based neuro‐recommendation system for improving consumer purchase experience

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