Spatial Itemset Mining: A Framework to Explore Itemsets in Geographic Space

Sengstock, Christian; Gertz, Michael

doi:10.1007/978-3-642-40683-6_12

Cited by 2 publications

(2 citation statements)

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“…But it still remains a problem to handle more complex types of data. Recent researches have for example focused on mining spatial patterns Extending pattern mining to discover more complex and meaningful types of patterns .…”

Section: Research Opportunitiesmentioning

confidence: 99%

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A survey of itemset mining

Fournier‐Viger

Lin

et al. 2017

WIREs Data Min & Knowl

198

137

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Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented. WIREs Data Mining Knowl Discov 2017, 7:e1207. doi: 10.1002/widm.1207 This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules

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Section: Research Opportunitiesmentioning

confidence: 99%

“…Recent researches have for example focused on mining spatial patterns. 113 • Extending pattern mining to discover more complex and meaningful types of patterns. Related to the above opportunity, another important issue to discover more complex types of patterns.…”

Section: Research Opportunitiesmentioning

confidence: 99%