Contrasting the Contrast Sets: An Alternative Approach

Satsangi,; Zaiane,

doi:10.1109/ideas.2007.4318095

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Formally, a PCS between two groups is one that satisfies the condition: max(support(cset,G 0 ), support(cset,G 1 )) ≥ minSup (adapted from Satsangi and Zaiane [35]). A significant contrast set (SCS) is a PCS that also meets the significance condition.…”

Section: Contrast Setsmentioning

confidence: 99%

“…2 When the itemset was missing from one of the datasets (i.e., it was not frequent), then the count of support and non-support was unknown. In such a case the support frequency for the contingency table (S Gij or S Nij ) was calculated [35] according to the supCount formula: Following Bay and Pazzani [5], if a PCS was found to be significant at α=0.05, then the patch it represents was deemed valuable (see online appendix for a sensitivity analysis). A valuable patch which was predominately visited by users from the goal group was known as a goal patch and placed in the set P Gi , whereas visitation mostly from the non-goal group resulted in a patch being labeled as a non-goal patch and being placed in the set P Ni .…”

Section: Determining Patch Valuementioning

confidence: 99%

See 1 more Smart Citation

Goal attainment on long tail web sites: An information foraging approach

McCart¹,

Padmanabhan²,

Berndt³

2013

Decision Support Systems

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a b s t r a c tThe long tail has attracted substantial theoretical as well as practical interest, yet there have been few empirical studies that have explicitly examined the factors that drive online conversions at these sites. This research tests several hypotheses derived from Information Foraging Theory (IFT) that pertain to goal achievement on long tail Web sites. IFT introduced concepts of information patches and information scent to model information seeking behavior of individuals, but has mostly been tested in production rule environments where the theory is used to simulate user behavior. Testing IFT-driven hypotheses on real data required learning information patches and scents using an inductive approach and in this paper we adapt existing algorithms for these discovery tasks. Our results based on clickstream data from forty-seven small business Web sites show both the existence of valuable information patches and information scent trails as well as their importance in explaining conversion on these sites. The majority of the hypotheses were supported and we discuss the implications of this for researchers and practitioners.

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Section: Contrast Setsmentioning

confidence: 99%

Section: Determining Patch Valuementioning

confidence: 99%

Goal attainment on long tail web sites: An information foraging approach

McCart¹,

Padmanabhan²,

Berndt³

2013

Decision Support Systems

View full text Add to dashboard Cite

show abstract

“…Zaiane and his collegues Satsangi and Zaiane 2007) have worked on discovering so called contrast sequences. This is similar to a single stage of the interactive model building process described here, except that two datasets are compared, while in our approach one of the datasets is replaced by an HMM.…”

Section: Related Researchmentioning

confidence: 99%

Using interesting sequences to interactively build Hidden Markov Models

Jaroszewicz

2010

Data Min Knowl Disc

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The paper presents a method of interactive construction of global Hidden Markov Models (HMMs) based on local sequence patterns discovered in data. The method is based on finding interesting sequences whose frequency in the database differs from that predicted by the model. The patterns are then presented to the user who updates the model using their intelligence and their understanding of the modelled domain. It is demonstrated that such an approach leads to more understandable models than automated approaches. Two variants of the problem are considered: mining patterns occurring only at the beginning of sequences and mining patterns occurring at any position; both practically meaningful. For each variant, algorithms have been developed allowing for efficient discovery of all sequences with given minimum interestingness. Applications to modelling webpage visitors behavior and to modelling protein secondary structure are presented, validating the proposed approach.

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“…Kralj et al (2007a, b) performed contrast set mining through subgroup discovery (Lavrac et al 2004). Satsangi and Zaiane (2007) use the association rule-based approach to find contrast sets. At the same time, STUCCO has been used to address real-world problems in many fields, such as education (Minaei-Bidgoli et al 2004), medical research (Kralj et al 2007a, b;Siu et al 2005), and the aviation industry (Nazeri et al 2008).…”

Section: Related Workmentioning

confidence: 99%

A Tree-Based Contrast Set-Mining Approach to Detecting Group Differences

Liu

Yang

Chen

et al. 2014

INFORMS Journal on Computing

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U nderstanding differences between groups in a data set is one of the fundamental tasks in data analysis.As relevant applications accumulate, data-mining methods have been developed to specifically address the problem of group difference detection. Contrast set mining discovers group differences in the form of conjunction of feature-value pairs or items. In this paper, we incorporate absolute difference, relative difference, and statistical significance in our definition of a group difference, and develop a novel method named DIFF that uses the prefix-tree structure to compress the search space, follows a tree traversal procedure to discover the complete set of significant group differences, and employs efficient pruning strategies to expedite the search process. We conducted comprehensive experiments to compare our method with existing methods on completeness of results, pruning efficiency, and computational efficiency. The experiments demonstrate that our method guarantees completeness of results and achieves higher pruning efficiency and computational efficiency compared to STUCCO. In addition, our definition of group difference is more general than STUCCO. Our method is more effective than traditional approaches, such as classification trees, in discovering the complete set of significant group differences.

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Contrasting the Contrast Sets: An Alternative Approach

Cited by 8 publications

References 7 publications

Goal attainment on long tail web sites: An information foraging approach

Goal attainment on long tail web sites: An information foraging approach

Using interesting sequences to interactively build Hidden Markov Models

A Tree-Based Contrast Set-Mining Approach to Detecting Group Differences

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