1,591 research outputs found
Subjectively Interesting Subgroup Discovery on Real-valued Targets
Deriving insights from high-dimensional data is one of the core problems in
data mining. The difficulty mainly stems from the fact that there are
exponentially many variable combinations to potentially consider, and there are
infinitely many if we consider weighted combinations, even for linear
combinations. Hence, an obvious question is whether we can automate the search
for interesting patterns and visualizations. In this paper, we consider the
setting where a user wants to learn as efficiently as possible about
real-valued attributes. For example, to understand the distribution of crime
rates in different geographic areas in terms of other (numerical, ordinal
and/or categorical) variables that describe the areas. We introduce a method to
find subgroups in the data that are maximally informative (in the formal
Information Theoretic sense) with respect to a single or set of real-valued
target attributes. The subgroup descriptions are in terms of a succinct set of
arbitrarily-typed other attributes. The approach is based on the Subjective
Interestingness framework FORSIED to enable the use of prior knowledge when
finding most informative non-redundant patterns, and hence the method also
supports iterative data mining.Comment: 12 pages, 10 figures, 2 tables, conference submissio
Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns
Understanding customer buying patterns is of great interest to the retail
industry and has shown to benefit a wide variety of goals ranging from managing
stocks to implementing loyalty programs. Association rule mining is a common
technique for extracting correlations such as "people in the South of France
buy ros\'e wine" or "customers who buy pat\'e also buy salted butter and sour
bread." Unfortunately, sifting through a high number of buying patterns is not
useful in practice, because of the predominance of popular products in the top
rules. As a result, a number of "interestingness" measures (over 30) have been
proposed to rank rules. However, there is no agreement on which measures are
more appropriate for retail data. Moreover, since pattern mining algorithms
output thousands of association rules for each product, the ability for an
analyst to rely on ranking measures to identify the most interesting ones is
crucial. In this paper, we develop CAPA (Comparative Analysis of PAtterns), a
framework that provides analysts with the ability to compare the outcome of
interestingness measures applied to buying patterns in the retail industry. We
report on how we used CAPA to compare 34 measures applied to over 1,800 stores
of Intermarch\'e, one of the largest food retailers in France
Statistical strategies for pruning all the uninteresting association rules
We propose a general framework to describe formally the
problem of capturing the intensity of implication for
association rules through statistical metrics.
In this framework we present properties that influence the
interestingness of a rule, analyze the conditions that
lead a measure to perform a perfect prune at a time,
and define a final proper order to sort the surviving
rules. We will discuss why none of the currently employed
measures can capture objective interestingness, and
just the combination of some of them, in a multi-step fashion,
can be reliable. In contrast, we propose a new simple modification
of the Pearson coefficient that will meet all the necessary
requirements. We statistically infer the convenient cut-off
threshold for this new metric by empirically describing its
distribution function through simulation. Final experiments
serve to show the ability of our proposal.Postprint (published version
New probabilistic interest measures for association rules
Mining association rules is an important technique for discovering meaningful
patterns in transaction databases. Many different measures of interestingness
have been proposed for association rules. However, these measures fail to take
the probabilistic properties of the mined data into account. In this paper, we
start with presenting a simple probabilistic framework for transaction data
which can be used to simulate transaction data when no associations are
present. We use such data and a real-world database from a grocery outlet to
explore the behavior of confidence and lift, two popular interest measures used
for rule mining. The results show that confidence is systematically influenced
by the frequency of the items in the left hand side of rules and that lift
performs poorly to filter random noise in transaction data. Based on the
probabilistic framework we develop two new interest measures, hyper-lift and
hyper-confidence, which can be used to filter or order mined association rules.
The new measures show significantly better performance than lift for
applications where spurious rules are problematic
GCG: Mining Maximal Complete Graph Patterns from Large Spatial Data
Recent research on pattern discovery has progressed from mining frequent
patterns and sequences to mining structured patterns, such as trees and graphs.
Graphs as general data structure can model complex relations among data with
wide applications in web exploration and social networks. However, the process
of mining large graph patterns is a challenge due to the existence of large
number of subgraphs. In this paper, we aim to mine only frequent complete graph
patterns. A graph g in a database is complete if every pair of distinct
vertices is connected by a unique edge. Grid Complete Graph (GCG) is a mining
algorithm developed to explore interesting pruning techniques to extract
maximal complete graphs from large spatial dataset existing in Sloan Digital
Sky Survey (SDSS) data. Using a divide and conquer strategy, GCG shows high
efficiency especially in the presence of large number of patterns. In this
paper, we describe GCG that can mine not only simple co-location spatial
patterns but also complex ones. To the best of our knowledge, this is the first
algorithm used to exploit the extraction of maximal complete graphs in the
process of mining complex co-location patterns in large spatial dataset.Comment: 1
Knowledge Discovery in Documents by Extracting Frequent Word Sequences
published or submitted for publicatio
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