713 research outputs found
An efficient closed frequent itemset miner for the MOA stream mining system
Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version
Mining Frequent Graph Patterns with Differential Privacy
Discovering frequent graph patterns in a graph database offers valuable
information in a variety of applications. However, if the graph dataset
contains sensitive data of individuals such as mobile phone-call graphs and
web-click graphs, releasing discovered frequent patterns may present a threat
to the privacy of individuals. {\em Differential privacy} has recently emerged
as the {\em de facto} standard for private data analysis due to its provable
privacy guarantee. In this paper we propose the first differentially private
algorithm for mining frequent graph patterns.
We first show that previous techniques on differentially private discovery of
frequent {\em itemsets} cannot apply in mining frequent graph patterns due to
the inherent complexity of handling structural information in graphs. We then
address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling
based algorithm. Unlike previous work on frequent itemset mining, our
techniques do not rely on the output of a non-private mining algorithm.
Instead, we observe that both frequent graph pattern mining and the guarantee
of differential privacy can be unified into an MCMC sampling framework. In
addition, we establish the privacy and utility guarantee of our algorithm and
propose an efficient neighboring pattern counting technique as well.
Experimental results show that the proposed algorithm is able to output
frequent patterns with good precision
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
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