Tree Structures for Mining Association Rules

Coenen, Frans; Goulbourne, Graham; Leng, Paul

doi:10.1023/b:dami.0000005257.93780.3b

Cited by 103 publications

(56 citation statements)

References 6 publications

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“…PADUA has as participants agents with distinct datasets of records relating to a classification problem. These agents produce reasons for and against classifications by mining association rules from their datasets using standard data mining techniques [13,14,15]. By "association rule" we mean no more than that the antecedent is a set of reasons for believing the consequent.…”

Section: Padua Protocolmentioning

confidence: 99%

“…These other classifiers were: 1. TFPC: TFPC, Total From Partial Classification ( [26], [27]), is a Classification Association Rule Mining (CARM) algorithm founded on the TFP (Total From Partial) Association Rule Mining (ARM) algorithm ( [28], [29]); which, in turn, is an extension of the Apriori-T [12] (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: Cross Validation and Comparison With Other Classifiersmentioning

confidence: 99%

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PADUA: a protocol for argumentation dialogue using association rules

2009

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Abstract:We describe PADUA, a protocol designed to support two agents debating a classification by offering arguments based on association rules mined from individual datasets. We motivate the style of argumentation supported by PADUA, and describe the protocol. We discuss the strategies and tactics that can be employed by agents participating in a PADUA dialogue. PADUA is applied to a typical problem in the classification of routine claims for a hypothetical welfare benefit. We particularly address the problems that arise from the extensive number of misclassified examples typically found in such domains, where the high error rate is a widely recognised problem. We give examples of the use of PADUA in this domain, and explore in particular the effect of intermediate predicates. We have also done a large scale evaluation designed to test the effectiveness of using PADUA to detect misclassified examples, and to provide a comparison with other classification systems.

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Section: Padua Protocolmentioning

confidence: 99%

Section: Cross Validation and Comparison With Other Classifiersmentioning

confidence: 99%

PADUA: a protocol for argumentation dialogue using association rules

2009

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“…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%

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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“…Fuzzy ARM with fuzzy partitions with normalization (CFARM2). For standard ARM, the Apriori-TFP algorithm was used [6] with a range of support thresholds. As expected the number of frequent itemsets increases as the minimum support decreases.…”

Section: An Example Applicationmentioning

confidence: 99%

“…The objective of ARM is to identify patterns expressed in the form of Association Rules (ARs) in transaction data sets [5,6,12]. The attributes in ARM data sets are usually binary valued but ARM has also been applied to quantitative and categorical (non-binary) data [1,13,16].…”

Section: Introductionmentioning

confidence: 99%

Mining Fuzzy Association Rules from Composite Items

Khan

Muyeba

Coenen

Artificial Intelligence in Theory and Practice II

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Abstract. A novel framework is described for mining fuzzy Association Rules (ARs) relating the properties of composite attributes, i.e. attributes or items that each feature a number of values derived from a common schema. To apply fuzzy Association Rule Mining (ARM) we partition the property values into fuzzy property sets. This paper describes: (i) the process of deriving the fuzzy sets (Composite Fuzzy ARM or CFARM) and (ii) a unique property ARM algorithm founded on the correlation factor interestingness measure. The paper includes a complete analysis, demonstrating: (i) the potential of fuzzy property ARs, and (ii) that a more succinct set of property ARs (than that generated using a non-fuzzy method) can be produced using the proposed approach.

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Tree Structures for Mining Association Rules

Cited by 103 publications

References 6 publications

PADUA: a protocol for argumentation dialogue using association rules

PADUA: a protocol for argumentation dialogue using association rules

Arguing from Experience to Classifying Noisy Data

Mining Fuzzy Association Rules from Composite Items

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