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Efficient mining of understandable patterns from multivariate interval time series

Fabian Mörchen, Alfred Ultsch
2007 Data mining and knowledge discovery  
We present a new method for the understandable description of local temporal relationships in multivariate data, called Time Series Knowledge Mining (TSKM).  ...  We define the Time Series Knowledge Representation (TSKR) as a new language for expressing temporal knowledge in time interval data.  ...  The first author was partly supported by Siemens Corporate Research, Princeton, NJ during this time.  ... 
doi:10.1007/s10618-007-0070-1 fatcat:j2ijrwv6tnag3gzeqypuhyn36i

Unsupervised pattern mining from symbolic temporal data

Fabian Mörchen
2007 SIGKDD Explorations  
For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data.  ...  For univariate data and limited gaps suffix tree methods are more efficient. Recently, efficient algorithms have been proposed to mine the more general concept of partial order from time points.  ...  First a multivariate symbolic interval series is obtained from numeric time series. Event patterns describe several more or less simultaneous intervals and express the concept of synchronicity.  ... 
doi:10.1145/1294301.1294302 fatcat:rwcvkifhknh2reo6zp2tkco4vq

Visualizing frequent patterns in large multivariate time series

M. Hao, M. Marwah, H. Janetzko, R. Sharma, D. A. Keim, U. Dayal, D. Patnaik, N. Ramakrishnan
2011 Visualization and Data Analysis 2011  
., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation.  ...  To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events.  ...  In general, we use the above methods to map a multivariate time series to frequent patterns.  ... 
doi:10.1117/12.872169 dblp:conf/vda/HaoMJSKDPR11 fatcat:hwhmc6m44vdq3cuevbcjiany7y

Visual exploration of frequent patterns in multivariate time series

M. C. Hao, M. Marwah, H. Janetzko, U. Dayal, D. A. Keim, D. Patnaik, N. Ramakrishnan, R. K. Sharma
2012 Information Visualization  
Since a large time series can contain hundreds of motifs, there is a need to support interactive analysis and exploration.  ...  The detection of frequently occurring patterns, also called motifs, in data streams has been recognized as an important task.  ...  Pattern Finding in Large Multivariate Time Series A schematic overview of our approach is provided in Figure 2 .  ... 
doi:10.1177/1473871611430769 fatcat:xsxorbfbg5fn3iijpmu6kks7z4

Discovery of Temporal Association Rules in Multivariate Time Series

Yi ZHAO, Ting-ting ZHANG
2018 DEStech Transactions on Computer Science and Engineering  
This paper reviews some methods for temporal association rule mining, and proposes two similar algorithms for the mining of frequent patterns in single and multivariate time series, both scalable and efficient  ...  This paper focuses on mining association rules in multivariate time series.  ...  The whole process includes time series representation, pattern mining in single timer series and clustering, pattern mining in multivariate time series and temporal association rule generation.  ... 
doi:10.12783/dtcse/mmsta2017/19653 fatcat:qsjp2fc56vav7gdclrz234iq6i

Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review [article]

Maria Bampa
2019 arXiv   pre-print
According to the literature retrieved the main methods were found to fall into 5 different approaches: based on temporal abstraction, graph-based, learning weights and data tables containing time series  ...  Based on a review of the existing literature, 11 articles from the last 10 years were chosen to be studied.  ...  Converting the data from time-stamped to a series of uniform time intervals can benefit the pattern mining process. [8] Both studies by Moskovitch et al.  ... 
arXiv:1904.04940v1 fatcat:hce6dhg3g5dl5j77nighkz5wpq

Procedural Steps for Knowledge Mining in Time Series

Kaustuva ChandraDev, Sibananda Behera
2013 International Journal of Computer Applications  
The Hierarchical Time series Knowledge Representation (HTKR) is the hierarchical language which expresses the temporal aspects of coincidence and partial order, for interval patterns.  ...  We present mining procedural steps which are more efficient, effective and based on item set techniques.  ...  Allen's relations are not advantageous for pattern discovery from interval time series, as shown by the following examples.  ... 
doi:10.5120/10525-5508 fatcat:74slcxik6jbnzk6zzkw7nylp4m

Classification of multivariate time series via temporal abstraction and time intervals mining

Robert Moskovitch, Yuval Shahar
2014 Knowledge and Information Systems  
data points into a series of symbolic time intervals; (2) mining these intervals to discover frequent temporal patterns, using Allen's 13 temporal relations; (3) using the patterns as features to induce  ...  Classification of multivariate time series data, often including both time points and intervals at variable frequencies, is a challenging task.  ...  For insightful discussions time intervals mining and classification using TIRPs our thanks to Christos Faloutsos, Christian Freksa, Fabian Moerchen, Dhaval Patel and Iyad Batel.  ... 
doi:10.1007/s10115-014-0784-5 fatcat:u4lp5ywtubgk3flhbcyfi2ry4m

Classification-driven temporal discretization of multivariate time series

Robert Moskovitch, Yuval Shahar
2014 Data mining and knowledge discovery  
Increasingly, temporal abstraction, in which a series of raw-data time points is abstracted into a set of symbolic time intervals, is being used for classification of multivariate time series.  ...  data points into a series of symbolic time intervals (based on either unsupervised or supervised temporal abstraction); (2) mining these time intervals to discover frequent temporal-interval relation  ...  For insightful discussions of time intervals mining and classification using TIRPs, we wish to express our thanks to Christos Faloutsos, Christian Freksa, Fabian Moerchen, Dhaval Patel, and Iyad Batel.  ... 
doi:10.1007/s10618-014-0380-z fatcat:uobcfmyub5eyfh62gksnsawsci

Mining Temporal Exception Rules from Multivariate Time Series Using a new Support Measure

Thábata Amaral, Elaine P. M. de Sousa
2020 Journal of Information and Data Management  
We performed an extensive experimental analysis in real multivariate time series to verify the practical applicability of TRiER.  ...  We also present a new support measure to manipulate time series. This measure considers the context in which a pattern occurs, thus incorporating more semantics to the results obtained.  ...  BACKGROUND Time Series Time series is a sequence of time-ordered observations with regular time intervals between each pair of observations [Mitsa 2010 ].  ... 
doi:10.5753/jidm.2020.2020 fatcat:vch2uopsrrhgpalgbm5m5ektsa

Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

To mine customers' behavior in a time-variant database, the re-mining of the updated database is required that further increases processing cost in terms of execution time and memory space with every update  ...  In this paper, an optimized incremental technique is proposed that utilizes temporal association rule mining in a time-variant database for mining customer behavioral patterns in an updated database.  ...  Multivariate Time Series Definition 4 (Multivariate Time Series): A multivariate time series (MTS) [32] comprises of individual time series, denoted by (𝑇𝑆 1 , 2 ,𝑇𝑆 3 ,…,𝑇𝑆 k ).  ... 
doi:10.35940/ijitee.l2482.1081219 fatcat:evnbvsqiubhzxodqnfxmxldo4i

A Pattern Mining Approach for Classifying Multivariate Temporal Data

Iyad Batal, Hamed Valizadegan, Gregory F. Cooper, Milos Hauskrecht
2011 2011 IEEE International Conference on Bioinformatics and Biomedicine  
We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems.  ...  Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task.  ...  The problem of mining temporal patterns from time interval data is a relatively young research field.  ... 
doi:10.1109/bibm.2011.39 pmid:22267987 pmcid:PMC3261774 dblp:conf/bibm/BatalVCH11 fatcat:ix3sdpiwjngorczsi2mvhirzpa

Fast time intervals mining using the transitivity of temporal relations

Robert Moskovitch, Yuval Shahar
2013 Knowledge and Information Systems  
The mined symbolic time intervals can be part of the input, or can be generated by a temporalabstraction process from raw time-stamped data.  ...  Our experimental comparison of the KarmaLego algorithm's runtime performance to several existing state of the art time intervals pattern mining methods demonstrated a significant speed up, especially with  ...  intervals mining.  ... 
doi:10.1007/s10115-013-0707-x fatcat:gmau266rrjdvxb5h7kpj45sixq

Mining Signatures from Event Sequences

Rajput S.H., Chetan Jadhav, Yogesh Deshmukh, Sandip Sonawane, Hemant Jadhav
2015 IJARCCE  
The framework allows the presentation, extra4ction, and mining of high order latent occasion event structure and relationships between single and many sequences.  ...  This paper proposes a novel secular knowledge representation and learning framework to proposed largescale secular signature mining of longitudinal heterogeneous occasional data.  ...  [14] , [16] , [15] proposed a novel Time Series Knowledge Representation (TSKR) as a pattern language (grammar) for temporal knowledge discovery from multivariate time series and symbolic interval  ... 
doi:10.17148/ijarcce.2015.44129 fatcat:up6onqghvje7rgj6bi4o4aw7ky

An efficient pattern mining approach for event detection in multivariate temporal data

Iyad Batal, Gregory F. Cooper, Dmitriy Fradkin, James Harrison, Fabian Moerchen, Milos Hauskrecht
2015 Knowledge and Information Systems  
pattern mining is a special case of time-interval pattern mining, in which all intervals are simply time points with zero durations.  ...  This approach first converts the time series data into time-interval sequences of temporal abstractions.  ...  We presented an efficient algorithm that mines time-interval patterns backward in time, starting from patterns related to most recent observations.  ... 
doi:10.1007/s10115-015-0819-6 pmid:26752800 pmcid:PMC4704806 fatcat:7pelhltbpzbeziaptxekgzol5q
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