A Tool for Interactive Subgroup Discovery Using Distribution Rules

Lucas, Joel Pinho; Jorge, Alípio Mário; Pereira, Fernando; Pernas, Ana Marilza; Machado, Antônio Lilles Tavares

doi:10.1007/978-3-540-77002-2_36

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…However, rather than a representative value (e.g., the mean), DR identify unusual distributions of the target Lucas et al 2007). The approach finds subgroups, expressed as association rules with a statistical distribution on the consequent.…”

Section: Distribution Rulesmentioning

confidence: 99%

“…As mentioned before (Sect. 3.2), Distribution Rules are a SD method that looks for unusual target distributions Lucas et al 2007). Cortana and CAREN can be used for mining other structures of data.…”

Section: Comparison With Distribution Rulesmentioning

confidence: 99%

“…We handle the discretized numerical attributes as a nominal, not ordinal, scale. In terms of the property of interest (target), this numerical variable does not have to be previously discretized, because the method works with raw distributions (Lucas et al 2007).…”

Section: Comparison With Distribution Rulesmentioning

confidence: 99%

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Discovering a taste for the unusual: exceptional models for preference mining

et al. 2018

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Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes 'exceptional' varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge.

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Section: Distribution Rulesmentioning

confidence: 99%

Section: Comparison With Distribution Rulesmentioning

confidence: 99%

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Discovering a taste for the unusual: exceptional models for preference mining

et al. 2018

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“…To spark attention to the potential synergy of combining these fields with data mining, a recent workshop [4] brought together researchers from all these fields. When visualisation is used in data mining, this is often done after the search [5,6]. MIME [7] is an interactive tool that allows a user to explore itemsets, but only using traditional interestingness measures, which makes it still hard to find something that is subjectively interesting.…”

Section: Related Approachesmentioning

confidence: 99%

Interactive Data Exploration Using Pattern Mining

Leeuwen

2014

Lecture Notes in Computer Science

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Abstract. We live in the era of data and need tools to discover valuable information in large amounts of data. The goal of exploratory data mining is to provide as much insight in given data as possible. Within this field, pattern set mining aims at revealing structure in the form of sets of patterns. Although pattern set mining has shown to be an effective solution to the infamous pattern explosion, important challenges remain. One of the key challenges is to develop principled methods that allow user-and task-specific information to be taken into account, by directly involving the user in the discovery process. This way, the resulting patterns will be more relevant and interesting to the user. To achieve this, pattern mining algorithms will need to be combined with techniques from both visualisation and human-computer interaction. Another challenge is to establish techniques that perform well under constrained resources, as existing methods are usually computationally intensive. Consequently, they are only applied to relatively small datasets and on fast computers. The ultimate goal is to make pattern mining practically more useful, by enabling the user to interactively explore the data and identify interesting structure. In this paper we describe the state-of-the-art, discuss open problems, and outline promising future directions.

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