12,918 research outputs found
Incrementally Mining Temporal Patterns in Interval-based Databases
[[abstract]]In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20141030~20141101[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Shanhai, Chin
DISCOVERING PATTERNS FROM TEMPORAL DATABASES USING TEMPORAL ASSOCIATION RULE
Data mining is the process of discovering and examining data from diverse viewpoint, using automatic or semiautomatic techniques to remove knowledge or useful information and discover correlations or meaningful patterns and rules from large databases. One of the most vital characteristic missed by the traditional data mining systems is their capability to record and process time-varying aspects of the real world databases. . Temporal data mining, which mines or discovers knowledge and patterns from temporal databases, is an extension of data mining with capability to include time attribute analysis. The pattern discovery task of temporal data mining discovers all patterns of interest from a large dataset. This paper presents an overview of temporal data mining and focus on pattern discovery using temporal association rules
Discovering Utility-driven Interval Rules
For artificial intelligence, high-utility sequential rule mining (HUSRM) is a
knowledge discovery method that can reveal the associations between events in
the sequences. Recently, abundant methods have been proposed to discover
high-utility sequence rules. However, the existing methods are all related to
point-based sequences. Interval events that persist for some time are common.
Traditional interval-event sequence knowledge discovery tasks mainly focus on
pattern discovery, but patterns cannot reveal the correlation between interval
events well. Moreover, the existing HUSRM algorithms cannot be directly applied
to interval-event sequences since the relation in interval-event sequences is
much more intricate than those in point-based sequences. In this work, we
propose a utility-driven interval rule mining (UIRMiner) algorithm that can
extract all utility-driven interval rules (UIRs) from the interval-event
sequence database to solve the problem. In UIRMiner, we first introduce a
numeric encoding relation representation, which can save much time on relation
computation and storage on relation representation. Furthermore, to shrink the
search space, we also propose a complement pruning strategy, which incorporates
the utility upper bound with the relation. Finally, plentiful experiments
implemented on both real-world and synthetic datasets verify that UIRMiner is
an effective and efficient algorithm.Comment: Preprint. 11 figures, 5 table
FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences
We study the problem of classifying interval-based temporal sequences
(IBTSs). Since common classification algorithms cannot be directly applied to
IBTSs, the main challenge is to define a set of features that effectively
represents the data such that classifiers can be applied. Most prior work
utilizes frequent pattern mining to define a feature set based on discovered
patterns. However, frequent pattern mining is computationally expensive and
often discovers many irrelevant patterns. To address this shortcoming, we
propose the FIBS framework for classifying IBTSs. FIBS extracts features
relevant to classification from IBTSs based on relative frequency and temporal
relations. To avoid selecting irrelevant features, a filter-based selection
strategy is incorporated into FIBS. Our empirical evaluation on eight
real-world datasets demonstrates the effectiveness of our methods in practice.
The results provide evidence that FIBS effectively represents IBTSs for
classification algorithms, which contributes to similar or significantly better
accuracy compared to state-of-the-art competitors. It also suggests that the
feature selection strategy is beneficial to FIBS's performance.Comment: In: Big Data Analytics and Knowledge Discovery. DaWaK 2020. Springer,
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