Unsupervised discovery of basic human actions from activity recording datasets

Mohammad, Yasser; Nishida, Toyoaki

doi:10.1109/sii.2012.6426960

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…Note that the motif pair discovery problem has been extensively studied in the last decade (Yeh et al, 2016;Zhu et al, 2016;Mueen and Chavoshi, 2015;Li et al, 2015;Mueen et al, 2009;Mohammad and Nishida, 2014;Mohammad and Nishida, 2012). The reason is that if we want to find a collection of recurrent subsequences in T , the most computationally expensive operation consists of identifying the motif pairs (Zhu et al, 2016), namely, solving Problem 1.…”

Section: Motif Discoverymentioning

confidence: 99%

Matrix profile goes MAD: variable-length motif and discord discovery in data series

Linardi

Zhu

Palpanas

et al. 2020

Data Min Knowl Disc

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In the last fifteen years, data series motif and discord discovery have emerged as two useful and well-used primitives for data series mining, with applications to many domains, including robotics, entomology, seismology, medicine, and climatology. Nevertheless, the state-of-the-art motif and discord discovery tools still require the user to provide the relative length. Yet, in several cases, the choice of length is critical and unforgiving. Unfortunately, the obvious brute-force solution, which tests all lengths within a given range, is computationally untenable. In this work, we introduce a new framework, which provides an exact and scalable motif and discord discovery algorithm that efficiently finds all motifs and discords in a given range of lengths. We evaluate our approach with five diverse real datasets, and demonstrate that it is up to 20 times faster than the state-of-the-art. Our results also show that removing the unrealistic assumption that the user knows the correct length, can often produce more intuitive and actionable results, which could have otherwise been missed.

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Section: Motif Discoverymentioning

confidence: 99%

Matrix profile goes MAD: variable-length motif and discord discovery in data series

Linardi

Zhu

Palpanas

et al. 2020

Data Min Knowl Disc

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“…Notice also that MOEN uses only zscore normalization and cannot be used (without modification) with range normalization in contrast with MN which can be used with any normalization scheme that follows (2) and that MOEN does not guarantee the return of the requested number of motifs while MN guarantees this number of motifs which is an important advantage when exact motif discovery is used as a step in a general motif discovery algorithm [17] or in a clustering algorithm [29]. In our experiments, the maximum number of motifs returned by MOEN was 3 compared with the 15 returned by all other algorithms.…”

Section: Mnmentioning

confidence: 99%

“…Figure 1 shows two occurrences of a motif in a time series. Several algorithms have been proposed for solving this problem [5,7,11,12,14,17,26,32].…”

Section: Introductionmentioning

confidence: 99%

Exact multi-length scale and mean invariant motif discovery

Mohammad

Nishida

2015

Appl Intell

Self Cite

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Discovering approximately recurrent motifs (ARMs) in timeseries is an active area of research in data mining. Exact motif discovery is defined as the problem of efficiently finding the most similar pairs of timeseries subsequences and can be used as a basis for discovering ARMs. The most efficient algorithm for solving this problem is the MK algorithm which was designed to find a single pair of timeseries subsequences with maximum similarity at a known length. This paper provides three extensions of the MK algorithm that allow it to find the top K similar subsequences at multiple lengths using both the Euclidean distance metric and scale invariant normalized version of it. The proposed algorithms are then applied to both synthetic data and real-world data with a focus on discovery of ARMs in human motion trajectories.

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