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An association rule (AR) is a common knowledge model in data mining that describes an implicative co-occurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent rArr consequent" rule. A variant of the AR is the weighted association rule (WAR). With regard to a marketing context, this paper introduces a...
With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window...
INGENS is a prototype of GIS which integrates a geographic knowledge discovery engine to mine several kinds of spatial KDD objects from the topographic maps stored in a spatial database. In this paper we describe the main principles of an inductive spatial database in INGENS. Inductive database allows to keep permanent KDD objects and integrate database technology with systems for the geographic knowledge...
In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. In this paper we propose a new approach to prune and filter discovered rules. Using Domain Ontologies, we strengthen the integration of user knowledge in the post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task....
The ultimate goal of knowledge discovery (KD) is to extract sets of patterns leading to useful knowledge for obtaining user desirable outcomes. The key characteristics of knowledge usefulness is that these patterns are actionable. In the last decade, KD algorithms such as mining for association rules, clustering, and classification rules, have made a tremendous progress and have been demonstrated...
Association rules is a technique that can detect patterns within the items of a dataset. The constrained version applies several restrictions that reduces the number of rules and also helps improve performance. On the other hand, OLAP statistical tests is an integration of exploratory On-Line Analytical Processing techniques and statistical tests. It uses a different approach that make it more appropriate...
This article introduces ARUBAS, a new framework to build associative classifiers. In contrast with many existing associative classifiers, it uses class association rules to transform the feature space and uses instance-based reasoning to classify new instances. The framework allows the researcher to use any association rule mining algorithm to produce the class association rules. Every aspect of the...
On-line data stream mining has attracted much research interest, but systems that can be used as a workbench for online mining have not been researched, since they pose many difficult research challenges. The proposed system addresses these challenges by an architecture based on three main technical advances, (i) introduction of new constructs and synoptic data structures whereby complex KDD queries...
There are several algorithms proposed for maintaining the sequential patterns as records are inserted. In addition to record insertion, the pattern maintenance for record modification is also very important in the real-applications. In the past, we have proposed the fast updated sequential pattern tree (called FUSP tree) structure for handling record insertion. In this paper, we attempt to handle...
The theoretical relationship between association rules and machine learning techniques needs to be studied in more depth. This article studies the use of clustering as a model for association rule mining. The clustering model is exploited to bound and estimate association rule support and confidence. We first study the efficient computation of the clustering model with K-means; we show the sufficient...
In data mining problems, data is usually provided in the form of data tables. To represent knowledge discovered from data tables, decision logic (DL) is proposed in rough set theory. While DL is an instance of propositional logic, we can also describe data tables by other logical formalisms. In this paper, we use a kind of many-sorted logic, called attribute value-sorted logic, to study association...
Relations of logical calculi of association rules to measures of interestingness of association rules are studied. Logical calculi of association rules, 4ft-quantifiers and important classes of association rules are briefly introduced. New 4ft-quantifiers and association rules are defined by applications of suitable thresholds to several known measures of interestingness. It is proved that some of...
Sequential pattern mining has become more and more popular in recent years due to its wide applications and the fact that it can find more information than association rules. Two famous algorithms in sequential pattern mining are AprioriAll and PrefixSpan. These two algorithms not only need to scan a database or projected-databases many times, but also require setting a minimal support threshold to...
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