Data structure for association rule mining: T-trees and P-trees

Coenen, Frans; Leng, Paul; Ahmed, Shakil

doi:10.1109/tkde.2004.8

Cited by 115 publications

(48 citation statements)

References 4 publications

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“…-TFPC: TFPC, Total From Partial Classification [6], is a Classification Association Rule Mining (CARM) algorithm founded on the TFP (Total From Partial) Association Rule Mining (ARM) algorithm; which, in turn, is an extension of the Apriori-T (Apriori Total) ARM algorithm [1]. TFPC is designed to produce Classification Association Rules (CARs) whereas Apriori-T and TFP are designed to generate Association Rules (ARs).…”

Section: Discussionmentioning

confidence: 99%

“…Using their distinct databases PISA agents produce reasons for and against classifications by mining Association Rules (ARs) from their datasets using Association Rule Mining (ARM) techniques [1], [6]. ARs [1] are probabilistic relationships expressed as rules of the form X → Y read as if X is true then Y is likely to be true, or X is a reason to think Y is true where X and Y are disjoint subsets of some global set of attributes.…”

Section: Arguing From Experiencementioning

confidence: 99%

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Multi-Party Argument from Experience

Wardeh

Bench‐Capon

Coenen

2010

Lecture Notes in Computer Science

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Abstract. A framework, PISA, for conducting dialogues to resolve disputes concerning the correct categorisation of particular cases, is described. Unlike previous systems to conduct such dialogues, which have typically involved only two agents, PISA allows any number of agents to take part, facilitating discussion of cases which permit many possible categorizations. A particular feature of the framework is that the agents argue directly from individual repositories of experiences rather than from a previously engineered knowledge base, as is the usual case, and so the knowledge engineering bottleneck is avoided. Argument from experience is enabled by real time data-mining conducted by individual agents to find reasons to support their viewpoints, and critique the arguments of other parties. Multiparty dialogues raise a number of significant issues, necessitating appropriate design choices. The paper describes how these issues were resolved in the protocol and implemented in PISA, and illustrates the operation of PISA using an example based of a dataset relating to nursery provision. Finally some experiments comparing PISA with other classifiers are reported.

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Section: Discussionmentioning

confidence: 99%

Section: Arguing From Experiencementioning

confidence: 99%

Multi-Party Argument from Experience

Wardeh

Bench‐Capon

Coenen

2010

Lecture Notes in Computer Science

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“…As noted above the distinguishing feature of both PADUA and PISA was that the arguments used by the agents were derived directly from a database of previous examples using ARM [26]. In PADUA the background dataset of each agent was represented by the means of a T-tree (Total tree) data structure, a reverse set enumeration tree structure with fast look up properties [9].…”

Section: Classification Through Argumentation Using Pisa and Paduamentioning

confidence: 99%

“…In the context of this paper the antecedent of an AR represents a set of reasons for believing the example should be classified as expressed in the consequent. Neither PADUA nor PISA use a specialized CARM algorithm, instead they are found on the Apriori T ARM algorithm described in [9] and then classify the test cases by the means of the dialogue.…”

Section: Classification Through Argumentation Using Pisa and Paduamentioning

confidence: 99%

“…Rule Mining (CARM) algorithm founded on the TFP (Total From Partial) ARM algorithm ( [12], [13]); which, in turn, is an extension of the Apriori-T (Apriori Total) ARM algorithm. TFPC is designed to produce Classification Association Rules (CARs) whereas Apriori-T and TFP are designed to generate Association Rules (ARs).…”

Section: Tfpc (Total From Partial Classification) ([10] [11]) Is a mentioning

confidence: 99%

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Arguing from Experience to Classifying Noisy Data

Wardeh

Coenen

Bench‐Capon

2009

Data Warehousing and Knowledge Discovery

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Abstract.A process, based on argumentation theory, is described for classifying very noisy data. More specifically a process founded on a concept called "arguing from experience" is described where by several software agents "argue" about the classification of a new example given individual "case bases" containing previously classified examples. Two "arguing from experience" protocols are described: PADUA which has been applied to binary classification problems and PISA which has been applied to multi-class problems. Evaluation of both PADUA and PISA indicates that they operate with equal effectiveness to other classification systems in the absence of noise. However, the systems out-perform comparable systems given very noisy data.

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Improved methods for extracting frequent itemsets from interim‐support trees

et al. 2008

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Abstract:In this paper we present the Dual Support Apriori for Temporal data (DSAT) algorithm. This is a novel technique for discovering Jumping Emerging Patterns (JEPs) from time series data using a sliding window technique. Our approach is particularly effective when performing trend analysis in order to explore the itemset variations over time. Our proposed framework is different from the previous work on JEP in that we do not rely on itemsets borders with a constrained search space. DSAT exploits previously mined time stamped data by using a sliding window concept, thus requiring less memory, minimum computational cost and very low dataset accesses. DSAT discovers all JEPs, as in "naïve" approaches, but utilises less memory and scales linearly with large datasets sets as demonstrated in the experimental section.

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Data structure for association rule mining: T-trees and P-trees

Cited by 115 publications

References 4 publications

Multi-Party Argument from Experience

Multi-Party Argument from Experience

Arguing from Experience to Classifying Noisy Data

Improved methods for extracting frequent itemsets from interim‐support trees

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