scholarly journals FREQUENT CORRELATED PERIODIC PATTERN MINING FOR LARGE VOLUME SET USING TIME SERIES DATA

2014 ◽  
Vol 10 (10) ◽  
pp. 2105-2116 ◽  
Author(s):  
G. M. Karthik ◽  
S. Karthik
Keyword(s):  
Time Series ◽  
Large Volume ◽  
Pattern Mining ◽  
Time Series Data ◽  
Series Data ◽  
Periodic Pattern

Clustering of Time Series Data

2011 ◽  
pp. 258-263
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Series Data ◽  
Science And Engineering ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, that take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.

scholarly journals Motivic Pattern Extraction in Music, and Application to the Study of Tunisian Modal Music

2007 ◽  
Vol Volume 6, april 2007, joint... ◽  
Author(s):  
Olivier Lartillo ◽  
Mondher Ayari
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Basic Mechanism ◽  
Series Data ◽  
Periodic Pattern ◽  
Global Pattern ◽  
Basic Principles ◽  
Discovery System ◽  
International Audience ◽  
Modal Music

International audience A new methodology for automated extraction of repeated patterns in time-series data is presented, aimed in particular at the analysis of musical sequences. The basic principles consists in a search for closed patterns in a multi-dimensional parametric space. It is shown that this basic mechanism needs to be articulated with a periodic pattern discovery system, implying therefore a strict chronological scanning of the time-series data. Thanks to this modelling global pattern filtering may be avoided and rich and highly pertinent results can be obtained. The modelling has been integrated in a collaborative pro ject between ethnomusicology, cognitive sciences and computer science, aimed at the study of Tunisian Modal Music. Une méthodologie d'extraction automatique de motifs répétés dans des séquences temporelles est présentée, dédiée en particulier à l'analyse de séquences musicales. L'approche initiale consiste en une recherche de motifs fermés dans un espace paramétrique multidimensionnel. Il est montré que ce premier mécanisme doit être articulé avec un système de découverte de motifs périodiques, ce qui implique un parcours strictement chronologique de la séquence. Cette modélisation permet d'éviter un filtrage global des patterns, et donc d'obtenir des résultats présentant une richesse et une pertinence élevée. La modélisation a été intégrée au sein d'un projet collaboratif entre ethnomusicologie, sciences cognitives et informatique, dédié à l'étude de la musique modale tunisienne.

scholarly journals Data Mining Techniques using Time Series Research

2019 ◽  
Vol 8 (2S11) ◽  
pp. 121-129
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Research Field ◽  
Series Data ◽  
Primary Role ◽  
Useful Knowledge ◽  
Time Series Data Mining ◽  
Using Data

As time-series data are eventually large the discovery of knowledge from these massive data seems to be a challenge issue. The similarity measure plays a primary role in time series data mining, which improves the accuracy of data mining task. Time series data mining is used to mine all useful knowledge from the profile of data. Obviously, we have a potential to perform these works, but it leads to a vague crisis. This paper involves a survey regarding time series technique and its related issues like challenges, preprocessing methods, pattern mining and rule discovery using data mining. Streaming of data is one of the difficult tasks that should be managed over time. Thus, this paper can provide a basic and prominent knowledge about time series in data mining research field.

scholarly journals Study on Visual Techniques of Potential Pattern Discovery for Time Series Data

2018 ◽  
Vol 232 ◽  
pp. 02049
Author(s):  
Dalin Xu ◽  
Yingmei Wei
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Mining Methods ◽  
Visualization Technology ◽  
Important Branch ◽  
Development And Evolution

Sequential pattern mining is always a very important branch of time series data mining. The pattern mining with visual means can be used to extract the knowledge of time series data more intuitively. Based on the research content, this paper analyzes the sequence pattern mining methods in different aspects and their combination with visualization technology. We further discuss and summarize the advantages of different visualization methods in discovering the potential patterns in time series data. Different systems and models have their unique information to show the focus. Compared with the characteristics of the model, the development and evolution of visualization technology for the discovery of potential patterns of time series data can be summarized. Finally, this paper discusses its development trend and how to play a greater role in the era of big data.

scholarly journals TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 365
Author(s):  
Qiang Zhao ◽  
Qing Li ◽  
Deshui Yu ◽  
Yinghua Han
Keyword(s):  
Time Series ◽  
Association Rules ◽  
Pattern Mining ◽  
Time Series Data ◽  
Series Data ◽  
Association Rules Mining ◽  
The Public ◽  
Mining Algorithm ◽  
Temporal Relationships ◽  
Multiple Variables

In many industrial domains, there is a significant interest in obtaining temporal relationships among multiple variables in time-series data, given that such relationships play an auxiliary role in decision making. However, when transactions occur frequently only for a period of time, it is difficult for a traditional time-series association rules mining algorithm (TSARM) to identify this kind of relationship. In this paper, we propose a new TSARM framework and a novel algorithm named TSARM-UDP. A TSARM mining framework is used to mine time-series association rules (TSARs) and an up-to-date pattern (UDP) is applied to discover rare patterns that only appear in a period of time. Based on the up-to-date pattern mining, the proposed TSAR-UDP method could extract temporal relationship rules with better generality. The rules can be widely used in the process industry, the stock market, etc. Experiments are then performed on the public stock data and real blast furnace data to verify the effectiveness of the proposed algorithm. We compare our algorithm with three state-of-the-art algorithms, and the experimental results show that our algorithm can provide greater efficiency and interpretability in TSARs and that it has good prospects.

scholarly journals Information Processing on Compressed Data

2021 ◽  
pp. 89-104
Author(s):  
Yoshimasa Takabatake ◽  
Tomohiro I ◽  
Hiroshi Sakamoto
Keyword(s):  
Time Series ◽  
Information Processing ◽  
Pattern Mining ◽  
Time Series Data ◽  
Privacy Preserving ◽  
Series Data ◽  
Work Related ◽  
Wide Range ◽  
Similarity Computation ◽  
Compressed Data

AbstractWe survey our recent work related to information processing on compressed strings. Note that a “string” here contains any fixed-length sequence of symbols and therefore includes not only ordinary text but also a wide range of data, such as pixel sequences and time-series data. Over the past two decades, a variety of algorithms and their applications have been proposed for compressed information processing. In this survey, we mainly focus on two problems: recompression and privacy-preserving computation over compressed strings. Recompression is a framework in which algorithms transform a given compressed data into another compressed format without decompression. Recent studies have shown that a higher compression ratio can be achieved at lower cost by using an appropriate recompression algorithm such as preprocessing. Furthermore, various privacy-preserving computation models have been proposed for information retrieval, similarity computation, and pattern mining.

scholarly journals High Influencing Pattern Discovery over Time Series Data

2021 ◽  
Vol 10 (10) ◽  
pp. 696
Author(s):  
Dianwu Fang ◽  
Lizhen Wang ◽  
Jialong Wang ◽  
Meijiao Wang
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Spatial Distance ◽  
Series Data ◽  
Analytic Hierarchy ◽  
Location Pattern ◽  
Attribute Weights ◽  
Downward Closure ◽  
High Influence

A spatial co-location pattern denotes a subset of spatial features whose instances frequently appear nearby. High influence co-location pattern mining is used to find co-location patterns with high influence in specific aspects. Studies of such pattern mining usually rely on spatial distance for measuring nearness between instances, a method that cannot be applied to an influence propagation process concluded from epidemic dispersal scenarios. To discover meaningful patterns by using fruitful results in this field, we extend existing approaches and propose a mining framework. We first defined a new concept of proximity to depict semantic nearness between instances of distinct features, thus applying a star-shaped materialized model to mine influencing patterns. Then, we designed attribute descriptors to perceive attributes of instances and edges from time series data, and we calculated the attribute weights via an analytic hierarchy process, thereby computing the influence between instances and the influence of features in influencing patterns. Next, we constructed influencing metrics and set a threshold to discover high influencing patterns. Since the metrics do not satisfy the downward closure property, we propose two improved algorithms to boost efficiency. Extensive experiments conducted on real and synthetic datasets verified the effectiveness, efficiency, and scalability of our method.

Clustering of Time Series Data

2011 ◽  
pp. 172-175
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Series Data ◽  
Science And Engineering ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, which take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.

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