12,770 research outputs found
A Model-Based Frequency Constraint for Mining Associations from Transaction Data
Mining frequent itemsets is a popular method for finding associated items in
databases. For this method, support, the co-occurrence frequency of the items
which form an association, is used as the primary indicator of the
associations's significance. A single user-specified support threshold is used
to decided if associations should be further investigated. Support has some
known problems with rare items, favors shorter itemsets and sometimes produces
misleading associations.
In this paper we develop a novel model-based frequency constraint as an
alternative to a single, user-specified minimum support. The constraint
utilizes knowledge of the process generating transaction data by applying a
simple stochastic mixture model (the NB model) which allows for transaction
data's typically highly skewed item frequency distribution. A user-specified
precision threshold is used together with the model to find local frequency
thresholds for groups of itemsets. Based on the constraint we develop the
notion of NB-frequent itemsets and adapt a mining algorithm to find all
NB-frequent itemsets in a database. In experiments with publicly available
transaction databases we show that the new constraint provides improvements
over a single minimum support threshold and that the precision threshold is
more robust and easier to set and interpret by the user
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
Mining Event Logs to Support Workflow Resource Allocation
Workflow technology is widely used to facilitate the business process in
enterprise information systems (EIS), and it has the potential to reduce design
time, enhance product quality and decrease product cost. However, significant
limitations still exist: as an important task in the context of workflow, many
present resource allocation operations are still performed manually, which are
time-consuming. This paper presents a data mining approach to address the
resource allocation problem (RAP) and improve the productivity of workflow
resource management. Specifically, an Apriori-like algorithm is used to find
the frequent patterns from the event log, and association rules are generated
according to predefined resource allocation constraints. Subsequently, a
correlation measure named lift is utilized to annotate the negatively
correlated resource allocation rules for resource reservation. Finally, the
rules are ranked using the confidence measures as resource allocation rules.
Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and
the presented approach, and the results show that the presented approach is
effective in both accuracy and candidate resource recommendations.Comment: T. Liu et al., Mining event logs to support workflow resource
allocation, Knowl. Based Syst. (2012), http://dx.doi.org/
10.1016/j.knosys.2012.05.01
Finding the True Frequent Itemsets
Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It
requires to identify all itemsets appearing in at least a fraction of
a transactional dataset . Often though, the ultimate goal of
mining is not an analysis of the dataset \emph{per se}, but the
understanding of the underlying process that generated it. Specifically, in
many applications is a collection of samples obtained from an
unknown probability distribution on transactions, and by extracting the
FIs in one attempts to infer itemsets that are frequently (i.e.,
with probability at least ) generated by , which we call the True
Frequent Itemsets (TFIs). Due to the inherently stochastic nature of the
generative process, the set of FIs is only a rough approximation of the set of
TFIs, as it often contains a huge number of \emph{false positives}, i.e.,
spurious itemsets that are not among the TFIs. In this work we design and
analyze an algorithm to identify a threshold such that the
collection of itemsets with frequency at least in
contains only TFIs with probability at least , for some
user-specified . Our method uses results from statistical learning
theory involving the (empirical) VC-dimension of the problem at hand. This
allows us to identify almost all the TFIs without including any false positive.
We also experimentally compare our method with the direct mining of
at frequency and with techniques based on widely-used
standard bounds (i.e., the Chernoff bounds) of the binomial distribution, and
show that our algorithm outperforms these methods and achieves even better
results than what is guaranteed by the theoretical analysis.Comment: 13 pages, Extended version of work appeared in SIAM International
Conference on Data Mining, 201
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