On Handling Tree-Structured Attributes in Decision Tree Learning

Almuallim, Hussein; Akiba, Yasuhiro; Kaneda, Shigeo

doi:10.1016/b978-1-55860-377-6.50011-6

Cited by 19 publications

(12 citation statements)

References 7 publications

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“…The second part of the questionnaire, containing four questions (plus fields for free-text comments, as discussed later), was only filled in by students who indicated that they participated in the data mining task. 5 There were 33 Group S members and 41 Group W members (i.e. 74 out of 89) who filled in the second part.…”

Section: Discussionmentioning

confidence: 99%

“…While large-scale industrial projects merely focused on methodologies of leveraging the human expertise in different phases of the KDD cycle [27], academic research has, as early as at the beginning of 90s, occasionally attempted to formalise and subsequently automatically exploit such knowledge. First attempts to exploit prior conceptual 7 knowledge in propositional 8 machine learning (as research field predating present-day mainstream KDD) were often restricted to intra-attribute value (typically, taxonomical) structuring [5,8,31,43]. More sophisticated and abstract knowledge models were however sometimes also used to constrain the search and structure the learning workflow; examples are qualitative models by Clark & Matwin [14] or problem-solving methods [17,46].…”

Section: Related Workmentioning

confidence: 99%

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SEWEBAR-CMS: semantic analytical report authoring for data mining results

et al. 2010

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SEWEBAR-CMS is a set of extensions for the Joomla! Content Management System (CMS) that extends it with functionality required to serve as a communication platform between the data analyst, domain expert and the report user. SEWEBAR-CMS integrates with existing data mining software through PMML. Background knowledge is entered via a web-based elicitation interface and is preserved in documents conforming to the proposed Background Knowledge Exchange Format (BKEF) specification. SEWEBAR-CMS offers web service integration with semantic knowledge bases, into which PMML and BKEF data are stored. Combining domain knowledge and mining model visualizations with results of queries against the knowledge base, the data analyst conveys the results of the mining through a semiautomatically generated textual analytical report to the end user. The paper demonstrates the use of SEWEBAR-CMS on a real-world task from the cardiological domain and presents a user study showing that the proposed report authoring support leads to a statistically significant decrease in the time needed to author the analytical report.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SEWEBAR-CMS: semantic analytical report authoring for data mining results

et al. 2010

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“…Supporting hierarchical attributes is important in practice (see bullet point 1 above). A few algorithms have been designed to support hierarchical attributes directly (Almuallim, Akiba, & Kaneda, 1995;Aronis & Provost, 1997;Zhang, Silvescu, & Honavar, 2002), but they do not scale to large hierarchies. The process of automating the (now manual) process of utilizing hierarchies effectively still remains challenging.…”

Section: Support Hierarchical Attributesmentioning

confidence: 99%

Lessons and Challenges from Mining Retail E-Commerce Data

et al. 2004

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Abstract. The architecture of Blue Martini Software's e-commerce suite has supported data collection, data transformation, and data mining since its inception. With clickstreams being collected at the application-server layer, high-level events being logged, and data automatically transformed into a data warehouse using metadata, common problems plaguing data mining using weblogs (e.g., sessionization and conflating multi-sourced data) were obviated, thus allowing us to concentrate on actual data mining goals. The paper briefly reviews the architecture and discusses many lessons learned over the last four years and the challenges that still need to be addressed. The lessons and challenges are presented across two dimensions: business-level vs. technical, and throughout the data mining lifecycle stages of data collection, data warehouse construction, business intelligence, and deployment. The lessons and challenges are also widely applicable to data mining domains outside retail e-commerce.

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“…Finally, Thomas et al [21] and van Dompseler & van Someren [22] used problem-solving method descriptions (a kind of 'method ontologies') for the same purpose. There have also been several efforts to employ taxonomies over domains of individual attributes [3,4,13,19] to guide inductive learning. A recent contribution that goes in similar direction with our work but is more restricted in scope is that of ???…”

Section: Envisaged Tool Supportmentioning

confidence: 99%

Ontology-Enhanced Association Mining

Svátek

Rauch

Ralbovský

2006

Semantics, Web and Mining

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Abstract. The roles of ontologies in KDD are potentially manifold. We track them through different phases of the KDD process, from data understanding through task setting to mining result interpretation and sharing over the semantic web. The underlying KDD paradigm is association mining tailored to our 4ft-Miner tool. Experience from two different application domains-medicine and sociology-is presented throughout the paper.

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On Handling Tree-Structured Attributes in Decision Tree Learning

Cited by 19 publications

References 7 publications

SEWEBAR-CMS: semantic analytical report authoring for data mining results

SEWEBAR-CMS: semantic analytical report authoring for data mining results

Lessons and Challenges from Mining Retail E-Commerce Data

Ontology-Enhanced Association Mining

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