127,365 research outputs found
Sequence Pattern Mining with Variables
Sequence pattern mining (SPM) seeks to find multiple items that commonly occur together in a specific order. One common assumption is that all of the relevant differences between items are captured through creating distinct items, e.g., if color matters then the same item in two different colors would have two items created, one for each color. In some domains, that is unrealistic. This paper makes two contributions. The first extends SPM algorithms to allow item differentiation through attribute variables for domains with large numbers of items, e.g, by having one item with a variable with a color attribute rather than distinct items for each color. It demonstrates this by incorporating variables into Discontinuous Varied Order Sequence Mining (DVSM). The second contribution is the creation of Sequence Mining of Temporal Clusters (SMTC), a new SPM that addresses the interleaving issue common to SPM algorithms. Most SPM algorithms address interleaving by using a distance measure to separate co-occurring sequences. SMTC addresses interleaving by clustering all subsets of temporally close items and deferring the sequencing of mined patterns until the entire dataset if examined. Evaluation of the SPM algorithms on a digital forensics media analysis task results in a 96% reduction in terms to review, 100% detection of true positives and no false positives
Constraint-based sequence mining using constraint programming
The goal of constraint-based sequence mining is to find sequences of symbols
that are included in a large number of input sequences and that satisfy some
constraints specified by the user. Many constraints have been proposed in the
literature, but a general framework is still missing. We investigate the use of
constraint programming as general framework for this task. We first identify
four categories of constraints that are applicable to sequence mining. We then
propose two constraint programming formulations. The first formulation
introduces a new global constraint called exists-embedding. This formulation is
the most efficient but does not support one type of constraint. To support such
constraints, we develop a second formulation that is more general but incurs
more overhead. Both formulations can use the projected database technique used
in specialised algorithms. Experiments demonstrate the flexibility towards
constraint-based settings and compare the approach to existing methods.Comment: In Integration of AI and OR Techniques in Constraint Programming
(CPAIOR), 201
A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database
Constraint-based pattern discovery is at the core of numerous data mining
tasks. Patterns are extracted with respect to a given set of constraints
(frequency, closedness, size, etc). In the context of sequential pattern
mining, a large number of devoted techniques have been developed for solving
particular classes of constraints. The aim of this paper is to investigate the
use of Constraint Programming (CP) to model and mine sequential patterns in a
sequence database. Our CP approach offers a natural way to simultaneously
combine in a same framework a large set of constraints coming from various
origins. Experiments show the feasibility and the interest of our approach
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
An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming
The main advantage of Constraint Programming (CP) approaches for sequential
pattern mining (SPM) is their modularity, which includes the ability to add new
constraints (regular expressions, length restrictions, etc). The current best
CP approach for SPM uses a global constraint (module) that computes the
projected database and enforces the minimum frequency; it does this with a
filtering algorithm similar to the PrefixSpan method. However, the resulting
system is not as scalable as some of the most advanced mining systems like
Zaki's cSPADE. We show how, using techniques from both data mining and CP, one
can use a generic constraint solver and yet outperform existing specialized
systems. This is mainly due to two improvements in the module that computes the
projected frequencies: first, computing the projected database can be sped up
by pre-computing the positions at which an symbol can become unsupported by a
sequence, thereby avoiding to scan the full sequence each time; and second by
taking inspiration from the trailing used in CP solvers to devise a
backtracking-aware data structure that allows fast incremental storing and
restoring of the projected database. Detailed experiments show how this
approach outperforms existing CP as well as specialized systems for SPM, and
that the gain in efficiency translates directly into increased efficiency for
other settings such as mining with regular expressions.Comment: frequent sequence mining, constraint programmin
Constraining the Search Space in Temporal Pattern Mining
Agents in dynamic environments have to deal with complex situations including various temporal interrelations of actions and events. Discovering frequent patterns in such scenes can be useful in order to create prediction rules which can be used to predict future activities or situations. We present the algorithm MiTemP which learns frequent patterns based on a time intervalbased relational representation. Additionally the problem has also been transfered to a pure relational association rule mining task which can be handled by WARMR. The two approaches are compared in a number of experiments. The experiments show the advantage of avoiding the creation of impossible or redundant patterns with MiTemP. While less patterns have to be explored on average with MiTemP more frequent patterns are found at an earlier refinement level
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