Selecting the right interestingness measure for association patterns

Tan, Pang‐Ning; Kumar, Vipin; Srivastava, Jaideep

doi:10.1145/775047.775053

Cited by 644 publications

(432 citation statements)

References 9 publications

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“…Besides, we need measures which assign high weights for relations among specific terms and low weights for relations among generic terms. We can employ similarity (also called quality, interestingness or association) measures to compute relations among terms [7,27]. Different measures calculate the similarity between terms t i and t j (Ω(t i , t j )) considering the information contained in the contingency matrix presented in Table 1.…”

Section: Term Network Generationmentioning

confidence: 99%

Term Network Approach for Transductive Classification

Rossi¹,

Rezende²,

Lopes³

2015

Lecture Notes in Computer Science

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Abstract. Transductive classification is a useful way to classify texts when just few labeled examples are available. Transductive classification algorithms rely on term frequency to directly classify texts represented in vector space model or to build networks and perform label propagation. Related terms tend to belong to the same class and this information can be used to assign relevance scores of terms for classes and consequently the labels of documents. In this paper we propose the use of term networks to model term relations and perform transductive classification. In order to do so, we propose (i) different ways to generate term networks, (ii) how to assign initial relevance scores for terms, (iii) how to propagate the relevance scores among terms, and (iv) how to use the relevance scores of terms in order to classify documents. We demonstrate that transductive classification based on term networks can surpass the accuracies obtained by transductive classification considering texts represented in other types of networks or vector space model, or even the accuracies obtained by inductive classification. We also demonstrated that we can decrease the size of term networks through feature selection while keeping classification accuracy and decreasing computational complexity.

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Section: Term Network Generationmentioning

confidence: 99%

Term Network Approach for Transductive Classification

Rossi¹,

Rezende²,

Lopes³

2015

Lecture Notes in Computer Science

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“…In this new classification problem, each association rule R is seen as a query instance whose target feature value (which is either interesting or uninteresting) is unknown and whose determining features are the interestingness factors having the potential to determine the interestingness of R. There are so many objective interestingness factors influencing the interestingness of association rules, including support, confidence, coverage, strength and size of the rule. In the literature, some of them are also used as objective interestingness measures [35,46]. For instance, support and confidence can alone be used as objective interestingness measures [35,46].…”

Section: Modeling Interestingness As a Classification Problemmentioning

confidence: 99%

“…In the literature, some of them are also used as objective interestingness measures [35,46]. For instance, support and confidence can alone be used as objective interestingness measures [35,46].…”

Section: Modeling Interestingness As a Classification Problemmentioning

confidence: 99%

“…For example, objective interestingness factor x can be multiplied by the square of another objective interestingness factor y to obtain an objective interestingness measure xy 2 . Objective interestingness factors can also be used as an objective interestingness measure alone (e.g., confidence) [35,46]. Discovered patterns having interestingness value greater than the threshold are regarded as ''interesting".…”

Section: Introductionmentioning

confidence: 99%

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Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Aydın

Güvenir

2009

Knowledge-Based Systems

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a b s t r a c tIn a typical application of association rule learning from market basket data, a set of transactions for a fixed period of time is used as input to rule learning algorithms. For example, the well-known Apriori algorithm can be applied to learn a set of association rules from such a transaction set. However, learning association rules from a set of transactions is not a one time only process. For example, a market manager may perform the association rule learning process once every month over the set of transactions collected through the last month. For this reason, we will consider the problem where transaction sets are input to the system as a stream of packages. The sets of transactions may come in varying sizes and in varying periods. Once a set of transactions arrive, the association rule learning algorithm is executed on the last set of transactions, resulting in new association rules. Therefore, the set of association rules learned will accumulate and increase in number over time, making the mining of interesting ones out of this enlarging set of association rules impractical for human experts. We refer to this sequence of rules as ''association rule set stream" or ''streaming association rules" and the main motivation behind this research is to develop a technique to overcome the interesting rule selection problem. A successful association rule mining system should select and present only the interesting rules to the domain experts. However, definition of interestingness of association rules on a given domain usually differs from one expert to another and also over time for a given expert. This paper proposes a post-processing method to learn a subjective model for the interestingness concept description of the streaming association rules. The uniqueness of the proposed method is its ability to formulate the interestingness issue of association rules as a benefit-maximizing classification problem and obtain a different interestingness model for each user. In this new classification scheme, the determining features are the selective objective interestingness factors related to the interestingness of the association rules, and the target feature is the interestingness label of those rules. The proposed method works incrementally and employs user interactivity at a certain level. It is evaluated on a real market dataset. The results show that the model can successfully select the interesting ones.

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“…However, other technique exist (see [8] for a survey), including statistical methods such as interest ( [9]) and conviction ( [10]) that are often combined with support. We have successfully applied our methods to these, but do not discuss them here for lack of space.…”

Section: Background and Related Workmentioning

confidence: 99%

Removing Statistical Biases in Unsupervised Sequence Learning

Horman

Kaminka

2005

Lecture Notes in Computer Science

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Abstract. Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize the data. Popular approaches to such learning include statistical analysis and frequency based methods. We empirically compare these approaches and find that both approaches suffer from biases toward shorter sequences, and from inability to group together multiple instances of the same pattern. We provide methods to address these deficiencies, and evaluate them extensively on several synthetic and real-world data sets. The results show significant improvements in all learning methods used.

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Selecting the right interestingness measure for association patterns

Cited by 644 publications

References 9 publications

Term Network Approach for Transductive Classification

Term Network Approach for Transductive Classification

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Removing Statistical Biases in Unsupervised Sequence Learning

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