## Figures and Tables from this paper

## 25 Citations

A Genetic Algorithm to Discover Flexible Motifs with Support

- Computer Science2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)
- 2016

This paper revise the notion of motif support, characterizing it as the number of patterns or repetitions that define a motif, and proposes GENMOTIF, a genetic algorithm to discover motifs with support which is flexible enough to accommodate other motif specifications and task characteristics.

Spatial-time motifs discovery

- Computer ScienceIntell. Data Anal.
- 2020

CSA outperforms traditional methods designed only for time series and was able to prioritize motifs that were meaningful both in the context of synthetic data and also according to seismic specialists.

Ranking and significance of variable-length similarity-based time series motifs

- Computer ScienceExpert Syst. Appl.
- 2016

Survey on time series motif discovery

- Computer ScienceWIREs Data Mining Knowl. Discov.
- 2017

This contribution provides a review of the existing publications in time series motif discovery along with advantages and disadvantages of existing approaches and serves as a glossary for researchers in this field.

Discovery of DNA Motif Utilising an Integrated Strategy Based on Random Projection and Particle Swarm Optimization

- Computer ScienceMathematical Problems in Engineering
- 2019

A particle swarm optimization and random projection-based algorithm (PSORPS) is proposed for recognizing DNA motifs and the two operators of associated drift and independent drift are performed on the optimization results obtained by PSO.

NSAMD: A new approach to discover structured contiguous substrings in sequence datasets using Next-Symbol-Array

- Computer ScienceComput. Biol. Chem.
- 2016

A new binary hybrid particle swarm optimization with wavelet mutation

- Computer ScienceKnowl. Based Syst.
- 2017

Localizing Periodicity in Time Series and Videos

- Computer ScienceBMVC
- 2016

This work proposes a method that, given a time series representing a periodic signal that has a non-periodic prefix and tail, estimates the start, the end and the period of the periodic part of the signal.

An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent

- Computer ScienceWorld Wide Web
- 2016

A novel hybrid quantum swarm intelligence indexing method (HQSII) from the perspective of link prediction is proposed for the first time, which includes an optimal weight algorithm (OWA) and a fluctuation detection algorithm (FDA) and the otherness of micro node evolutions is considered into link prediction.

Empowering particle swarm optimization algorithm using multi agents' capability: A holonic approach

- Computer ScienceKnowl. Based Syst.
- 2017

## References

SHOWING 1-10 OF 65 REFERENCES

Constrained Motif Discovery in Time Series

- Computer ScienceNew Generation Computing
- 2009

The Constrained Motif Discovery problem is defined which enables utilization of domain knowledge into the motif discovery process and two algorithms called MCFull and MCInc are provided for efficiently solving the constrained motif discovery problem.

Exact Discovery of Time Series Motifs

- Computer ScienceSDM
- 2009

For the first time, a tractable exact algorithm to find time series motifs is shown and it is shown that this algorithm is fast enough to be used as a subroutine in higher level data mining algorithms for anytime classification, near-duplicate detection and summarization.

Efficient Proper Length Time Series Motif Discovery

- Computer Science2013 IEEE 13th International Conference on Data Mining
- 2013

This work proposes a novel algorithm using compression ratio as a heuristic to discover meaningful motifs in proper lengths using time series motifs as a hypothesis and demonstrates that the proposed method outperforms existing works in various domains both in terms of speed and accuracy.

Finding Motifs in Time Series

- Computer ScienceKDD 2002
- 2002

An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases and could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification.

Multiresolution Motif Discovery in Time Series

- Computer ScienceSDM
- 2010

This work fully exploit state of the art iSAX representation multiresolution capability to obtain motifs at different resolutions and yields interactivity, allowing the user to navigate along the Top-K motifs structure, allowing a deeper understanding of the time series database.

Probabilistic discovery of time series motifs

- Computer ScienceKDD '03
- 2003

This work introduces a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences, which is probabilistic in nature, but can find time series motifs with very high probability even in the presence of noise or "don't care" symbols.

Detecting time series motifs under uniform scaling

- Computer ScienceKDD '07
- 2007

This work introduces a new algorithm that allows discovery of time series motifs with invariance to uniform scaling, and shows that it produces objectively superior results in several important domains.

Approximate variable-length time series motif discovery using grammar inference

- Computer ScienceMDMKDD '10
- 2010

A novel approach, based on grammar induction, is proposed for approximate variable-length time series motif discovery, which offers the advantage of discovering hierarchical structure, regularity and grammar from the data.

Exact Discovery of Length-Range Motifs

- Computer ScienceACIIDS
- 2014

This paper argues that it is not trivial to extend this MK algorithm to handle multiple motifs of variable lengths when constraints of maximum overlap are to be satisfied which is the case in many real world applications and proposes an extension of the MK algorithm called MK++ to handle these conditions.

Enumeration of Time Series Motifs of All Lengths

- Computer ScienceICDM
- 2013

An exact algorithm, called MOEN, to enumerate motifs, which is an order of magnitude faster than the naive algorithm and frees us from re-discovering the same motif at different lengths and tuning multiple data-dependent parameters.