Association Rules Extraction for the Identification of Functional Dependencies in Complex Technical Infrastructures

This paper presents a novel semi-automatic approach to construct conceptual ontologies over structured data by exploiting both the schema and content of the input dataset. It effectively combines two well-founded database and data mining techniques, i.e., functional dependency discovery and association rule mining, to support domain experts in the construction of meaningful ontologies, tailored to the analyzed data, by using Description Logic (DL). To this aim, functional dependencies are first discovered to highlight valuable conceptual relationships among attributes of the data schema (i.e., among concepts). The set of discovered correlations effectively support analysts in the assertion of the Tbox ontological statements (i.e., the statements involving shared data conceptualizations and their relationships). Then, the analyst-validated dependencies are exploited to drive the association rule mining process. Association rules represent relevant and hidden correlations among data content and they are used to provide valuable knowledge at the instance level. The pushing of functional dependency constraints into the rule mining process allows analysts to look into and exploit only the most significant data item recurrences in the assertion of the Abox ontological statements (i.e., the statements involving concept instances and their relationships).

Download Full-text

A Unified Hierarchy for Functional Dependencies, Conditional Functional Dependencies and Association Rules

Formal Concept Analysis - Lecture Notes in Computer Science ◽

10.1007/978-3-642-01815-2_9 ◽

2009 ◽

pp. 98-113 ◽

Cited By ~ 10

Author(s):

Raoul Medina ◽

Lhouari Nourine

Keyword(s):

Association Rules ◽

Functional Dependencies

Download Full-text

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.998-999.842 ◽

2014 ◽

Vol 998-999 ◽

pp. 842-845 ◽

Cited By ~ 1

Author(s):

Jia Mei Guo ◽

Yin Xiang Pei

Keyword(s):

Data Mining ◽

Association Rules ◽

Clustering Algorithm ◽

Original Data ◽

Data Set ◽

Association Rules Mining ◽

Fuzzy Association Rules ◽

Redundant Data ◽

Fuzzy Partitions ◽

Rules Extraction

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

Download Full-text

Association Rules Extraction Based on Support Vector Machines

2006 6th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2006.1714216 ◽

2006 ◽

Author(s):

Chaoyang Ma ◽

Jia Ren ◽

Hongye Su ◽

Jian Chu

Keyword(s):

Support Vector Machines ◽

Association Rules ◽

Support Vector ◽

Vector Machines ◽

Rules Extraction

Download Full-text

Mine Rule

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch141 ◽

2011 ◽

pp. 740-745

Author(s):

Rosa Meo ◽

Giuseppe Psaila

Keyword(s):

Data Mining ◽

Association Rules ◽

Efficient Algorithms ◽

Np Hard ◽

Widespread Application ◽

Large Databases ◽

Very Large Databases ◽

Rules Extraction ◽

Semantic Problem ◽

Interesting Generalization

Mining of association rules is one of the most adopted techniques for data mining in the most widespread application domains. A great deal of work has been carried out in the last years on the development of efficient algorithms for association rules extraction. Indeed, this problem is a computationally difficult task, known as NP-hard (Calders, 2004), which has been augmented by the fact that normally association rules are being extracted from very large databases. Moreover, in order to increase the relevance and interestingness of obtained results and to reduce the volume of the overall result, constraints on association rules are introduced and must be evaluated (Ng et al.,1998; Srikant et al., 1997). However, in this contribution, we do not focus on the problem of developing efficient algorithms but on the semantic problem behind the extraction of association rules (see Tsur et al. [1998] for an interesting generalization of this problem).

Download Full-text

Fuzzy Functional Dependencies and Fuzzy Association Rules

DataWarehousing and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-48298-9_25 ◽

1999 ◽

pp. 229-240 ◽

Cited By ~ 5

Author(s):

Yuping Yang ◽

Mukesh Singhal

Keyword(s):

Association Rules ◽

Functional Dependencies ◽

Fuzzy Association Rules

Download Full-text

The Hierarchies of Multivalued Attribute Domains and Corresponding Applications in Data Mining

Wireless Communications and Mobile Computing ◽

10.1155/2018/1789121 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Yuxia Lei ◽

Yushu Yan ◽

Yonghua Han ◽

Feng Jiang

Keyword(s):

Data Mining ◽

Association Rules ◽

Language Processing ◽

Common Knowledge ◽

Formal Concept ◽

Research Association ◽

Functional Dependencies ◽

Mobile Data ◽

Healthcare Data ◽

Machine Learning Models

In mobile computing, machine learning models for natural language processing (NLP) have become one of the most attractive focus areas in research. Association rules among attributes are common knowledge patterns, which can often provide potential and useful information such as mobile users' interests. Actually, almost each attribute is associated with a hierarchy of the domain. Given an relation R=(U,A) and any cut αa on the hierarchy for every attribute a, there is another rough relation RΦ, where Φ=(αa:a∈A). This paper will establish the connection between the functional dependencies in R and RΦ, propose the method for extracting reducts in RΦ, and demonstrate the implementation of proposed method on an application in data mining of association rules. The method for acquiring association rules consists of the following three steps: (1) translating natural texts into relations, by NLP; (2) translating relations into rough ones, by attributes analysis or fuzzy k-means (FKM) clustering; and (3) extracting association rules from concept lattices, by formal concept analysis (FCA). Our experimental results show that the proposed methods, which can be applied directly to regular mobile data such as healthcare data, improved quality, and relevance of rules.

Download Full-text