1,220 research outputs found
Analyzing Sequential Pattern Mining to Detect Calcium Peaks in Cardiomyocytes Data
This study examines sequential pattern mining and its applications in various fields. The previous research was conducted by examining signal data, from which calcium peaks were automatically detected and classified. Before the implementation of sequential pattern mining approach to find out patterns from a dataset of 102 signals, association rule mining, frequent itemsets, Apriori algorithm, and rule generation were explored.
Sequential pattern mining, including time constraints, are defined, before examining a knowledge-assisted sequential pattern analysis, from which certain points are considered, such as what is a sequential itemset.
The implementation phase consists of calculating what constitutes a candidate itemset. The findings are modified to work with a sequential rule mining algorithm, and the results are discussed afterwards
Using Answer Set Programming for pattern mining
Serial pattern mining consists in extracting the frequent sequential patterns
from a unique sequence of itemsets. This paper explores the ability of a
declarative language, such as Answer Set Programming (ASP), to solve this issue
efficiently. We propose several ASP implementations of the frequent sequential
pattern mining task: a non-incremental and an incremental resolution. The
results show that the incremental resolution is more efficient than the
non-incremental one, but both ASP programs are less efficient than dedicated
algorithms. Nonetheless, this approach can be seen as a first step toward a
generic framework for sequential pattern mining with constraints.Comment: Intelligence Artificielle Fondamentale (2014
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
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
Mining Target-Oriented Sequential Patterns with Time-Intervals
A target-oriented sequential pattern is a sequential pattern with a concerned
itemset in the end of pattern. A time-interval sequential pattern is a
sequential pattern with time-intervals between every pair of successive
itemsets. In this paper we present an algorithm to discover target-oriented
sequential pattern with time-intervals. To this end, the original sequences are
reversed so that the last itemsets can be arranged in front of the sequences.
The contrasts between reversed sequences and the concerned itemset are then
used to exclude the irrelevant sequences. Clustering analysis is used with
typical sequential pattern mining algorithm to extract the sequential patterns
with time-intervals between successive itemsets. Finally, the discovered
time-interval sequential patterns are reversed again to the original order for
searching the target patterns.Comment: 11 pages, 9 table
Reductions for Frequency-Based Data Mining Problems
Studying the computational complexity of problems is one of the - if not the
- fundamental questions in computer science. Yet, surprisingly little is known
about the computational complexity of many central problems in data mining. In
this paper we study frequency-based problems and propose a new type of
reduction that allows us to compare the complexities of the maximal frequent
pattern mining problems in different domains (e.g. graphs or sequences). Our
results extend those of Kimelfeld and Kolaitis [ACM TODS, 2014] to a broader
range of data mining problems. Our results show that, by allowing constraints
in the pattern space, the complexities of many maximal frequent pattern mining
problems collapse. These problems include maximal frequent subgraphs in
labelled graphs, maximal frequent itemsets, and maximal frequent subsequences
with no repetitions. In addition to theoretical interest, our results might
yield more efficient algorithms for the studied problems.Comment: This is an extended version of a paper of the same title to appear in
the Proceedings of the 17th IEEE International Conference on Data Mining
(ICDM'17
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