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Enhanced mining of association rules from data cubes
- In Proceedings of the 9 th ACM International Workshop on Data Warehousing and OLAP (DOLAP 2006
, 2006
"... On-line analytical processing (OLAP) provides tools to explore and navigate into data cubes in order to extract interesting information. Nevertheless, OLAP is not capable of explaining relationships that could exist in a data cube. Association rules are one kind of data mining techniques which finds ..."
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On-line analytical processing (OLAP) provides tools to explore and navigate into data cubes in order to extract interesting information. Nevertheless, OLAP is not capable of explaining relationships that could exist in a data cube. Association rules are one kind of data mining techniques which finds associations among data. In this paper, we propose a framework for mining inter-dimensional association rules from data cubes according to a sum-based aggregate measure more general than simple frequencies provided by the traditional COUNT measure. Our mining process is guided by a meta-rule context driven by analysis objectives and exploits aggregate measures to revisit the definition of support and confidence. We also evaluate the interestingness of mined association rules according to Lift and Loevinger criteria and propose an efficient algorithm for mining inter-dimensional association rules directly from a multidimensional data. Categories and Subject Descriptors: H.2.8 [Information systems]: Database applications—Data mining; H.4.2 [Information systems]: Types of systems—Decision support.
A Complexity Guided Algorithm for Association Rule Extraction on Fuzzy DataCubes
- IEEE TRANSACTION ON FUZZY SYSTEMS
, 2008
"... Abstract — The use of OLAP systems as data sources for data mining techniques has been deeply studied resulting in what is called OLAM (On-line Analytical Mining). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data coming from different sources, it has ..."
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Abstract — The use of OLAP systems as data sources for data mining techniques has been deeply studied resulting in what is called OLAM (On-line Analytical Mining). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data coming from different sources, it has become necessary for models to support imprecision. Therefore, we need OLAM methods able to deal with this imprecision. Association Rules are one of the most used data mining techniques. There are several proposals that permits the extraction of association rules on DataCubes, but few of them deal with imprecision in the process. The main problem observed in these proposals is the complexity of the rule set obtained. In this paper we present a novel association rule extraction method that works over a fuzzy multidimensional model that is able to represent and manage imprecise data. Our method deals with the problem of reducing the complexity of the result obtained by means of the use of fuzzy concepts and a hierarchical relation among them. I.
A Conceptual Model for Combining Enhanced OLAP and Data Mining Systems
"... Abstract — Online Analytical Processing (OLAP) was widely used to visualize complex data for efficient, interactive and meaningful analysis. Its power comes in visualizing huge operational data for interactive analysis. On the other hand, data mining techniques (DM) are strong at detecting patterns ..."
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Abstract — Online Analytical Processing (OLAP) was widely used to visualize complex data for efficient, interactive and meaningful analysis. Its power comes in visualizing huge operational data for interactive analysis. On the other hand, data mining techniques (DM) are strong at detecting patterns and mining knowledge from historical data. OLAP and DM is believed to be able to complement each other to analyze large data sets in decision support systems. Some recent researches have shown the benefits of combining OLAP with Data Mining. In this paper, we reviewed the coupling of OLAP and data mining in the literature and identified their limitations. We proposed a conceptual model that overcomes the existing limitations, and provides a way for combining enhanced OLAP with data mining systems. Furthermore, the proposed model offers directions to improving cube construction time and visualization over the data cube.
An Architecture for Integrated Online Analytical Mining
- Journal of Emerging Technologies in Web Intelligence
, 2011
"... is an essential element of the decision support system and permits decision makers to visualize huge operational data for quick, consistent, interactive and meaningful analysis. More recently, data mining techniques are also used together with OLAP to analyze large data sets which makes OLAP more us ..."
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is an essential element of the decision support system and permits decision makers to visualize huge operational data for quick, consistent, interactive and meaningful analysis. More recently, data mining techniques are also used together with OLAP to analyze large data sets which makes OLAP more useful and easier to apply in decision support systems. Several works in the past proved the likelihood and interest of integrating OLAP with data mining and as a result a new promising direction of Online Analytical Mining (OLAM) has emerged. In this paper, a variety of OLAM architectures in the literature were reviewed and the limitations in the previously reported work have been identified. Literature review reveals the fact that none of the previously reported OLAM architectures have integrated enhanced OLAP with data mining. We enhanced the
OLEMAR: An Online Environment for Mining Association Rules in Multidimensional Data
, 2008
"... Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting information under different perspectives and levels of granularity. Nevertheless, OLAP techniques do not allow the identification of relationships, group ..."
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Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting information under different perspectives and levels of granularity. Nevertheless, OLAP techniques do not allow the identification of relationships, groupings, or exceptions that could hold in a data cube. To that end, we propose to enrich OLAP techniques with data mining facilities to benefit from the capabilities they offer. In this chapter, we propose an online environment for mining association rules in data cubes. Our environment called OLEMAR (online environment for mining association rules), is designed to extract associations from multidimensional data. It allows the extraction of inter-dimensional association rules from data cubes according to a sum-based aggregate measure, a more general indicator than aggregate values provided by the traditional COUNT measure. In our approach, OLAP users are able to drive a mining process guided by a meta-rule, which meets their analysis objectives. In
Discovering dynamic classification hierarchies in olap dimensions
- in: ISMIS 2012 : 20th International Symposium on Methodologies for Intelligent System
, 2012
"... Abstract. The standard approach to OLAP requires measures and di-mensions of a cube to be known at the design stage. Besides, dimensions are required tobenon-volatile, balancedandnormalized.These constraints appear too rigid for many data sets, especially semi-structured ones, such as user-generated ..."
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Abstract. The standard approach to OLAP requires measures and di-mensions of a cube to be known at the design stage. Besides, dimensions are required tobenon-volatile, balancedandnormalized.These constraints appear too rigid for many data sets, especially semi-structured ones, such as user-generated content in social networks and other web applications. We enrich the multidimensional analysis of such data via content-driven discovery of dimensions and classification hierarchies. Discovered elements are dynamic by nature and evolve along with the underlying data set. We demonstrate the benefits of our approach by building a data ware-house for the public stream of the popular social network and microblog-ging service Twitter. Our approach allows to classify users by their activity, popularity, behavior as well as to organize messages by topic, impact, origin, method of generation, etc. Such capturing of the dy-namic characteristic of the data adds more intelligence to the analysis and extends the limits of OLAP.
Discovering OLAP Dimensions in Semi-Structured Data
"... OLAP cubes are obtained from the input data based on the available attributes and known relationships between them. Transforming the input into a set of measures distributed along a set of uniformly structured dimensions may be un-realistic for applications dealing with semi-structured data. We prop ..."
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OLAP cubes are obtained from the input data based on the available attributes and known relationships between them. Transforming the input into a set of measures distributed along a set of uniformly structured dimensions may be un-realistic for applications dealing with semi-structured data. We propose to extend the capabilities of OLAP via content-driven discovery of measures and dimensions in the original dataset. New elements are discovered by means of data min-ing and other techniques and are therefore expected to be of limited temporal validity. In this work we focus on the challenge of generating, maintaining, and querying such dis-covered elements of the cube. We demonstrate the power of our approach by providing OLAP to the public stream of user-generated content pro-vided by Twitter. We were able to enrich the original set with dynamic characteristics such as user activity, popular-ity, messaging behavior, as well as to classify messages by topic, impact, origin, method of generation, etc. Knowledge discovery techniques coupled with human expertise enable structural enrichment of the original data beyond the scope of the existing methods for obtaining multidimensional mod-els from relational or semi-structured data.
MINING TRIADIC ASSOCIATION RULES
"... The objective of this research is to extract triadic association rules from a triadic formal context K: = (K1, K2, K3, Y) where K1, K2 and K3 respectively represent the sets of objects, properties (or attributes) and conditions while Y is a ternary relation between these sets. Our approach consists ..."
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The objective of this research is to extract triadic association rules from a triadic formal context K: = (K1, K2, K3, Y) where K1, K2 and K3 respectively represent the sets of objects, properties (or attributes) and conditions while Y is a ternary relation between these sets. Our approach consists to define a procedure to map a set of dyadic association rules into a set of triadic ones. The advantage of the triadic rules compared to the dyadic ones is that they are less numerous and more compact than the second ones and convey a richer semantics of data. Our approach is illustrated through an example of ternary relation representing a set of Customers who purchase theirProducts from Suppliers. The algorithms and approach proposed have been validated with experimentations on large real datasets.
International Journal of Fuzzy Systems, Vol. x, No. y, month and year An Active Multidimensional Association Mining Framework with User Preference Ontology
"... Abstract 1 Business data are subject to change by time or by the modifications of business rules. New knowledge needs to be extracted to reflect the most up to date situations hence periodic or occasional re-mining is essential. This paper proposes an active multidimensional association mining frame ..."
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Abstract 1 Business data are subject to change by time or by the modifications of business rules. New knowledge needs to be extracted to reflect the most up to date situations hence periodic or occasional re-mining is essential. This paper proposes an active multidimensional association mining framework that incorporates with user preference ontology, which contains surrogate queries that represent frequently used queries in the query history log. The representative power and the user preference of the surrogate queries are derived and expressed in fuzzy linguistic terms. The construction of the ontology is demonstrated. How it can assist the active mining mechanism is also described. Specifically, the connection of the user preference ontology to the user profile in the enterprise database allows dispatching of new mining results to specific users automatically. A prototype implementation of the proposed system framework is provided and an effectiveness experiment for the user preference ontology is also conducted.
Deciding the Correct Usage of Database Queries, Data Mining and OLAP in an Applications
"... In the recent years, numbers of the studies have been done on different techniques of information retrieval. These retrieval techniques includes Database Queries, Data Mining and Online Analytical Processing (OLAP).The retrieved information is used for various purposes according to the different req ..."
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In the recent years, numbers of the studies have been done on different techniques of information retrieval. These retrieval techniques includes Database Queries, Data Mining and Online Analytical Processing (OLAP).The retrieved information is used for various purposes according to the different requirements. The retrieved information might be used for the purpose of Analysis, for the purpose of various users behavior prediction or for the purpose of Decision Support System (DSS).Now the confusion is that when to use Database Queries, when to use Data Mining and when to use Online Analytical Processing (OLAP).This paper elaborates the usage of Database queries, Data Mining, OLAP according to the user’s purpose, requirements at the particular instant. The paper also emphasized on performance of these techniques with appropriate examples. The goal of paper is to give the better clue to the user about the usage of techniques such as Database Queries, Data Mining and OLAP in an application to get the information in an easy way with efficient performance.