2,206 research outputs found
Categorization of interestingness measures for knowledge extraction
Finding interesting association rules is an important and active research
field in data mining. The algorithms of the Apriori family are based on two
rule extraction measures, support and confidence. Although these two measures
have the virtue of being algorithmically fast, they generate a prohibitive
number of rules most of which are redundant and irrelevant. It is therefore
necessary to use further measures which filter uninteresting rules. Many
synthesis studies were then realized on the interestingness measures according
to several points of view. Different reported studies have been carried out to
identify "good" properties of rule extraction measures and these properties
have been assessed on 61 measures. The purpose of this paper is twofold. First
to extend the number of the measures and properties to be studied, in addition
to the formalization of the properties proposed in the literature. Second, in
the light of this formal study, to categorize the studied measures. This paper
leads then to identify categories of measures in order to help the users to
efficiently select an appropriate measure by choosing one or more measure(s)
during the knowledge extraction process. The properties evaluation on the 61
measures has enabled us to identify 7 classes of measures, classes that we
obtained using two different clustering techniques.Comment: 34 pages, 4 figure
Evaluation and optimization of frequent association rule based classification
Deriving useful and interesting rules from a data mining system is an essential and important task. Problems
such as the discovery of random and coincidental patterns or patterns with no significant values, and the
generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness
of rules generated by data mining algorithms are actively and constantly being examined and developed. In this
paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms,
combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task
On Objective Measures of Rule Surprisingness
Most of the literature argues that surprisingness is an inherently subjective aspect of the discovered knowledge, which cannot be measured in objective terms. This paper departs from this view, and it has a twofold goal: (1) showing that it is indeed possible to define objective (rather than subjective) measures of discovered rule surprisingness; (2) proposing new ideas and methods for defining objective rule surprisingness measures
Combining Clustering techniques and Formal Concept Analysis to characterize Interestingness Measures
Formal Concept Analysis "FCA" is a data analysis method which enables to
discover hidden knowledge existing in data. A kind of hidden knowledge
extracted from data is association rules. Different quality measures were
reported in the literature to extract only relevant association rules. Given a
dataset, the choice of a good quality measure remains a challenging task for a
user. Given a quality measures evaluation matrix according to semantic
properties, this paper describes how FCA can highlight quality measures with
similar behavior in order to help the user during his choice. The aim of this
article is the discovery of Interestingness Measures "IM" clusters, able to
validate those found due to the hierarchical and partitioning clustering
methods "AHC" and "k-means". Then, based on the theoretical study of sixty one
interestingness measures according to nineteen properties, proposed in a recent
study, "FCA" describes several groups of measures.Comment: 13 pages, 2 figure
Explainable subgraphs with surprising densities : a subgroup discovery approach
The connectivity structure of graphs is typically related to the attributes of the nodes. In social networks for example, the probability of a friendship between any pair of people depends on a range of attributes, such as their age, residence location, workplace, and hobbies. The high-level structure of a graph can thus possibly be described well by means of patterns of the form `the subgroup of all individuals with a certain properties X are often (or rarely) friends with individuals in another subgroup defined by properties Y', in comparison to what is expected. Such rules present potentially actionable and generalizable insight into the graph.
We present a method that finds node subgroup pairs between which the edge density is interestingly high or low, using an information-theoretic definition of interestingness. Additionally, the interestingness is quantified subjectively, to contrast with prior information an analyst may have about the connectivity. This view immediatly enables iterative mining of such patterns. This is the first method aimed at graph connectivity relations between different subgroups. Our method generalizes prior work on dense subgraphs induced by a subgroup description. Although this setting has been studied already, we demonstrate for this special case considerable practical advantages of our subjective interestingness measure with respect to a wide range of (objective) interestingness measures
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