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A survey of temporal data mining
2006
Sadhana (Bangalore)
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Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the ...
In this article, we present an overview of techniques of temporal data mining. We mainly concentrate on algorithms for pattern discovery in sequential data streams. ...
These frequent periodic 1-patterns are used to grow the maximal sub-pattern tree for mining all partial periodic patterns. ...
doi:10.1007/bf02719780
fatcat:nqyjuthpmreclhj4lygqyxxmk4
A Comprehensive Survey of Pattern Mining: Challenges and Opportunities
2018
International Journal of Computer Applications
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In this regard periodic pattern mining was introduced. It finds out the patterns that appear periodically and frequently in the database. PPM [56] and Twain [57] proposed this periodic pattern. ...
However, there are several methods which discover patterns in large data that can be understood by human beings. ...
doi:10.5120/ijca2018916573
fatcat:dktrnmfvhzadzm5rhutwijfkvy
SMCA: a general model for mining asynchronous periodic patterns in temporal databases
2005
IEEE Transactions on Knowledge and Data Engineering
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A 4-phase algorithm is devised to discover periodic patterns from a time series database presented in vertical format. ...
Mining periodic patterns in time series databases is an important data mining problem with many applications. ...
Index Terms-Periodic pattern, asynchronous sequence, partial periodicity, temporal database. ae 1 INTRODUCTION P ATTERN mining plays an important role in data mining tasks. ...
doi:10.1109/tkde.2005.98
fatcat:ihp2n7aac5djdmrmlfowa6qd7y
Discovering similar patterns in time series
2000
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00
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(i) Each recurring pattern is associated with temporal information pertaining to its durations of periodic appearances in a series. ...
Partial periodic patterns are an important class of regularities that exist in a time series. A key property of these patterns is that they can start, stop, and restart anywhere within a series. ...
The purpose of this paper is to discover recurring patterns by addressing mining challenges. Recurring patterns are ubiquitous in very large datasets. ...
doi:10.1145/347090.347192
dblp:conf/kdd/Caraca-ValenteL00
fatcat:2m6xmosxefgvbigb3mybgocquu
Mining, indexing, and querying historical spatiotemporal data
2004
Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04
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We define the spatiotemporal periodic pattern mining problem and propose an effective and fast mining algorithm for retrieving maximal periodic patterns. ...
In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. ...
The discovery of multiple partial periodical patterns that do not appear in every periodic segment was first studied in [5] . ...
doi:10.1145/1014052.1014080
dblp:conf/kdd/MamoulisCKHTC04
fatcat:p6iglx54affkxbteyj3aq6g6eu
Complete Closed Time Intervals-Related Patterns Mining
2021
AAAI Conference on Artificial Intelligence
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the database. ...
Using temporal abstraction, various forms of sampled multivariate temporal data can be transformed into a uniform representation of symbolic time intervals, from which Time Intervals Related Patterns ( ...
This research was partially funded by a grant of the Israeli Ministry of Science and Technology 8760521. ...
dblp:conf/aaai/HarelM21
fatcat:ja4brn77ynaf3ff2ufxghiyha4
Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment
2019
JMIR Public Health and Surveillance
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Periodic pattern mining or periodicity detection is process of finding periodic patterns in time series database. ...
The types of periodicities are symbol periodicity, sequence periodicity and segment periodicity and they should be identified even in the presence of noise in the time series database. ...
Periodicity is very common practice in time series mining algorithms, since it is more likely trying to discover periodicity signal with no time limit. ...
doi:10.2196/11357
fatcat:b7jxylw54vaelhgn32bjvvx5ku
An Enhanced Algorithm for Association Rule Mining in Huge Temporal Database
2019
Zenodo
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The work proposed in this paper applies knowledge discovery techniques on a series of huge datasets obtained over a partition that contains a large number of transactions in the consecutive time period ...
The discovery of the association relationship among huge temporal database has been legendary to be helpful in selective selling, call analysis and business management. ...
INTRODUCTION Temporal Data Mining (TDM) is determined as the activity of looking for interesting correlations or patterns in large temporal datasets. ...
doi:10.5281/zenodo.3372570
fatcat:34v4zq44frbhxkfj2rx6ze73f4
Efficient mining of partial periodic patterns in time series database
1999
Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337)
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Partial periodicity search, i.e., search for partial periodic patterns in time-series databases, is an interesting data mining problem. ...
However, partial periodicity is very common in practice since it is more likely that only some of the time episodes may exhibit periodic patterns. ...
Many methods have been developed for searching periodicity patterns in large data sets [8] . ...
doi:10.1109/icde.1999.754913
dblp:conf/icde/HanDY99
fatcat:u4xieudvvfd7ldhrtiqccdi5lm
Mining Spatio-Temporal Patterns in Trajectory Data
2010
Journal of Information Processing Systems
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We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. ...
In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. ...
An efficient mining algorithm for retrieving a maximal periodic pattern was proposed and several problems including the discovering of a shifted or distorted pattern were addressed. ...
doi:10.3745/jips.2010.6.4.521
fatcat:pa7xxuep3zd7dbl4au52agem44
An Overview : Temporal - Side of Sequential Patterns Discovery
2013
International Journal of Data Mining & Knowledge Management Process
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In this paper temporal data mining concepts and tasks and mining sequential patterns algorithms are discussed and evaluated. ...
Patterns which will helps new arrival to Temporal Data Mining arena. ...
Periodicity is a very important temporal feature frequently identified among the transactions in temporal databases [20] , [22] , [23] . ...
doi:10.5121/ijdkp.2013.3101
fatcat:aft7sf6tm5gb7hxilakoi6dn2y
Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care
2017
Sensors
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We develop an efficient algorithm named PPFP-growth (Productive Periodic-Frequent Pattern-growth) to discover all productive-associated periodic frequent patterns using these measures. ...
In addition, we employ a correlation test on the generated patterns and introduce productive-associated periodic-frequent patterns as the set of correlated periodic-frequent items. ...
patterns that occur in partial or full cyclic databases. ...
doi:10.3390/s17050952
pmid:28445441
pmcid:PMC5461076
fatcat:nyp3g5oyafbotjapr7lje4q4ay
Mining Signatures from Event Sequences
2015
IJARCCE
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We have suggested clinical assessment for naked interactive knowledge discovery in large electronic health record databases. ...
INTRODUCTION Data mining can be explained as an process that extracts some new nontrivial information contained in large databases. the aim is to discover hidden patterns, unexpected trends or other relationships ...
This is a very flexible and instinctive framework for encoding the temporal knowledge data contained in the event sequences. ...
doi:10.17148/ijarcce.2015.44129
fatcat:up6onqghvje7rgj6bi4o4aw7ky
Noise Removal in Distributed Time Series Database Using Predominant Pattern Distribution Model
2013
IOSR Journal of Engineering
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The existing suffix tree based periodic pattern mining algorithm can detect symbol, sequence and segment periodicity in time series data with noise filters for diverse noise kinds. ...
Performance of proposed framework is measured and evaluated in terms of periodic pattern mining accuracy, noise distribution rate, and predominant pattern occurrence. ...
The objective of examining a Time-Series Database is to discover how common a periodic prototype (full or partial) is repetitive in a particular time interval. ...
doi:10.9790/3021-03220613
fatcat:h6et7b7bcrd43gwqotrn66anwa
Efficient Trajectory Pattern Mining for both Sparse and Dense Dataset
2010
International Journal of Computer Applications
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data means that the number of candidates to be considered becomes very large. ...
In this paper an efficient trajectory pattern mining is proposed by incorporating three key techniques. In this paper we have examined ways of partitioning data for trajectory pattern discovery. ...
Han et al. [8] developed a frequent pattern mining method for mining partial periodicity patterns that are frequent maximal patterns where each pattern appears in a fixed period with a fixed set of offsets ...
doi:10.5120/1378-1857
fatcat:gvtxgyczijchroon4ic5eq2cqq
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