24,716 research outputs found
Prefix-Projection Global Constraint for Sequential Pattern Mining
Sequential pattern mining under constraints is a challenging data mining
task. Many efficient ad hoc methods have been developed for mining sequential
patterns, but they are all suffering from a lack of genericity. Recent works
have investigated Constraint Programming (CP) methods, but they are not still
effective because of their encoding. In this paper, we propose a global
constraint based on the projected databases principle which remedies to this
drawback. Experiments show that our approach clearly outperforms CP approaches
and competes well with ad hoc methods on large datasets
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees
The tasks of extracting (top-) Frequent Itemsets (FI's) and Association
Rules (AR's) are fundamental primitives in data mining and database
applications. Exact algorithms for these problems exist and are widely used,
but their running time is hindered by the need of scanning the entire dataset,
possibly multiple times. High quality approximations of FI's and AR's are
sufficient for most practical uses, and a number of recent works explored the
application of sampling for fast discovery of approximate solutions to the
problems. However, these works do not provide satisfactory performance
guarantees on the quality of the approximation, due to the difficulty of
bounding the probability of under- or over-sampling any one of an unknown
number of frequent itemsets. In this work we circumvent this issue by applying
the statistical concept of \emph{Vapnik-Chervonenkis (VC) dimension} to develop
a novel technique for providing tight bounds on the sample size that guarantees
approximation within user-specified parameters. Our technique applies both to
absolute and to relative approximations of (top-) FI's and AR's. The
resulting sample size is linearly dependent on the VC-dimension of a range
space associated with the dataset to be mined. The main theoretical
contribution of this work is a proof that the VC-dimension of this range space
is upper bounded by an easy-to-compute characteristic quantity of the dataset
which we call \emph{d-index}, and is the maximum integer such that the
dataset contains at least transactions of length at least such that no
one of them is a superset of or equal to another. We show that this bound is
strict for a large class of datasets.Comment: 19 pages, 7 figures. A shorter version of this paper appeared in the
proceedings of ECML PKDD 201
HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach
In this paper we present a novel hybrid (arraybased layout and vertical
bitmap layout) database representation approach for mining complete Maximal
Frequent Itemset (MFI) on sparse and large datasets. Our work is novel in terms
of scalability, item search order and two horizontal and vertical projection
techniques. We also present a maximal algorithm using this hybrid database
representation approach. Different experimental results on real and sparse
benchmark datasets show that our approach is better than previous state of art
maximal algorithms.Comment: 8 Pages In the proceedings of 9th IEEE-INMIC 2005, Karachi, Pakistan,
200
Dynamic load balancing for the distributed mining of molecular structures
In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of
methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the
past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially
render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to
discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no
reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic
partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated
load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer
Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed
approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable
for large-scale, multi-domain, heterogeneous environments, such as computational grids
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
Knowledge Discovery in Documents by Extracting Frequent Word Sequences
published or submitted for publicatio
Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems
Frequent itemset mining leads to the discovery of associations and
correlations among items in large transactional databases. Apriori is a
classical frequent itemset mining algorithm, which employs iterative passes
over database combining with generation of candidate itemsets based on frequent
itemsets found at the previous iteration, and pruning of clearly infrequent
itemsets. The Dynamic Itemset Counting (DIC) algorithm is a variation of
Apriori, which tries to reduce the number of passes made over a transactional
database while keeping the number of itemsets counted in a pass relatively low.
In this paper, we address the problem of accelerating DIC on the Intel Xeon Phi
many-core system for the case when the transactional database fits in main
memory. Intel Xeon Phi provides a large number of small compute cores with
vector processing units. The paper presents a parallel implementation of DIC
based on OpenMP technology and thread-level parallelism. We exploit the
bit-based internal layout for transactions and itemsets. This technique reduces
the memory space for storing the transactional database, simplifies the support
count via logical bitwise operation, and allows for vectorization of such a
step. Experimental evaluation on the platforms of the Intel Xeon CPU and the
Intel Xeon Phi coprocessor with large synthetic and real databases showed good
performance and scalability of the proposed algorithm.Comment: Accepted for publication in Journal of Computing and Information
Technology (http://cit.fer.hr
Exploring the Evolution of Node Neighborhoods in Dynamic Networks
Dynamic Networks are a popular way of modeling and studying the behavior of
evolving systems. However, their analysis constitutes a relatively recent
subfield of Network Science, and the number of available tools is consequently
much smaller than for static networks. In this work, we propose a method
specifically designed to take advantage of the longitudinal nature of dynamic
networks. It characterizes each individual node by studying the evolution of
its direct neighborhood, based on the assumption that the way this neighborhood
changes reflects the role and position of the node in the whole network. For
this purpose, we define the concept of \textit{neighborhood event}, which
corresponds to the various transformations such groups of nodes can undergo,
and describe an algorithm for detecting such events. We demonstrate the
interest of our method on three real-world networks: DBLP, LastFM and Enron. We
apply frequent pattern mining to extract meaningful information from temporal
sequences of neighborhood events. This results in the identification of
behavioral trends emerging in the whole network, as well as the individual
characterization of specific nodes. We also perform a cluster analysis, which
reveals that, in all three networks, one can distinguish two types of nodes
exhibiting different behaviors: a very small group of active nodes, whose
neighborhood undergo diverse and frequent events, and a very large group of
stable nodes
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