Fuzzy association rule mining framework and its application to effective fuzzy associative classification

Kianmehr, Keivan; Kaya, Mehmet; ElSheikh, Abdallah M.; Jida, Jamal; Alhajj, Reda

doi:10.1002/widm.40

Cited by 19 publications

(4 citation statements)

References 33 publications

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“…The increase in spatial data and human limitations in analyzing spatial data in detail make querying spatial databases crucial for spatiotemporal applications. In recent years, many studies [2,5,26] have addressed the issue of performing data mining tasks on data warehouses. Some of them [26,27] are explicitly interested in mining patterns and association rules in data cubes.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In addition, conventional data mining techniques are insufficient in the area of spatiotemporal database applications because they often require intensive computations and involve complex differential equations and computational algorithms [2]. However, we need to perform effective and efficient querying with a colossal amount of spatiotemporal data.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, since spatial-temporal applications are complex, they are challenging to analyze with conventional logic approaches. Fuzzy logic can be used for situations in which conventional logic technologies are ineffective, such as applications [2,[12][13][14][15][16][17][18][19][20][21] and systems [22,23] that mathematical models cannot precisely describe, those with significant uncertainties or contradictory conditions, and linguistically controlled applications or systems. The concepts of SOLAP and fuzzy logic can be combined to benefit from both to provide an effective and efficient platform for spatiotemporal applications.…”

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confidence: 99%

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Modeling and Querying Fuzzy SOLAP-Based Framework

Keskin

Yazıcı

2022

IJGI

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Nowadays, with the rise of sensor technology, the amount of spatial and temporal data is increasing day by day. Modeling data in a structured way and performing effective and efficient complex queries has become more essential than ever. Online analytical processing (OLAP), developed for this purpose, provides appropriate data structures and supports querying multidimensional numeric and alphanumeric data. However, uncertainty and fuzziness are inherent in the data in many complex database applications, especially in spatiotemporal database applications. Therefore, there is always a need to support flexible queries and analyses on uncertain and fuzzy data, due to the nature of the data in these complex spatiotemporal applications. FSOLAP is a new framework based on fuzzy logic technologies and spatial online analytical processing (SOLAP). In this study, we use crisp measures as input for this framework, apply fuzzy operations to obtain the membership functions and fuzzy classes, and then generate fuzzy association rules. Therefore, FSOLAP does not need to use predefined sets of fuzzy inputs. This paper presents the method used to model the FSOLAP and manage various types of complex and fuzzy spatiotemporal queries using the FSOLAP framework. In this context, we describe how to handle non-spatial and fuzzy spatial queries, as well as spatiotemporal fuzzy query types. Additionally, while FSOLAP primarily includes historical data and associated queries and analyses, we also describe how to handle predictive fuzzy spatiotemporal queries, which typically require an inference mechanism.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Modeling and Querying Fuzzy SOLAP-Based Framework

Keskin

Yazıcı

2022

IJGI

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“…The approach combines the obtained partitions with predefined linguistic labels. Many algorithms for determining suitable fuzzy partitions and finding rules on the basis of heuristic optimization, using genetic algorithms and other techniques, can be found in the literature …”

Section: Finding Associations In Fuzzy Transactionsmentioning

confidence: 99%

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Marín

Ruiz

Sánchez

2016

WIREs Data Min & Knowl

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Looking for associations in data is one of the data mining tasks that has aroused more interest in the literature. In this area, incorporating concepts of fuzzy set theory is useful in problems where imprecision and/or uncertainty appear. In most of the existing approaches, fuzzy association rules are widely seen as fuzzy rules, which are very different in nature from association rules, so problems like fuzzy inclusion and cardinality have been seldom taken into account explicitly, mostly providing ad hoc solutions for capturing semantics. In contrast, in this study we have taken the more general and natural approach of considering the elements of the association rule mining framework and studying possible and sensible fuzzy extensions, referred here as fuzzy frameworks for mining associations. As fuzzy frameworks are abstract mathematical models, another key contribution of the article is the notion of interpretations as mappings between fuzzy frameworks and specific datasets. This general analysis of the field is completed with a study of various important aspects that arise when proposing quality measures in a fuzzy environment, as well as those related to computational issues. The work also includes a review of the fuzzy framework that arises from fuzzy transactions regarded as fuzzy subsets of items, and shows that many of the approaches on fuzzy association rules that exist in the literature can be placed in the context of a proper interpretation of that framework. WIREs Data Mining Knowl Discov 2016, 6:50–69. doi: 10.1002/widm.1176 This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Association Rules

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Incremental association rule mining: a survey

Nath

Bhattacharyya

Ghosh

2013

WIREs Data Min & Knowl

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Association rule mining is a computationally expensive task. Despite the huge processing cost, it has gained tremendous popularity due to the usefulness of association rules. Several efficient algorithms can be found in the literature. This paper provides a comprehensive survey on the state‐of‐the‐art algorithms for association rule mining, specially when the data sets used for rule mining are not static. Addition of new data to a data set may lead to additional rules or to the modification of existing rules. Finding the association rules from the whole data set may lead to significant waste of time if the process has started from the scratch. Several algorithms have been evolved to attend this important issue of the association rule mining problem. This paper analyzes some of them to tackle the incremental association rule mining problem. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Classification

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Fuzzy association rule mining framework and its application to effective fuzzy associative classification

Cited by 19 publications

References 33 publications

Modeling and Querying Fuzzy SOLAP-Based Framework

Modeling and Querying Fuzzy SOLAP-Based Framework

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Incremental association rule mining: a survey

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