Modified Gath–Geva clustering for fuzzy segmentation of multivariate time-series

Abonyi, János; Feil, Balazs; Németh, Sándor; Árva, P.

doi:10.1016/j.fss.2004.07.008

Cited by 116 publications

(93 citation statements)

References 21 publications

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“…Fuzzy clustering algorithms have showed a significant potential to address this category of problems. With this regard, Abonyi et al (2005) developed a modified GathGeva algorithm to divide time-varying multivariate data into segments by using fuzzy sets to represent such temporal segments. Duncan and Bryant (1996) proposed dynamic programming to determine a total number of intervals within the data, the location of these intervals and the order of the model within each segment.…”

Section: Experimental Data Sets-a Summarymentioning

confidence: 99%

“…The quality of such granulation (abstraction) of data is clearly related to the ability of this abstraction process to retain the essence of the original data (problem) while removing (hiding) all unnecessary details. With this regard, granularity of information (Bargiela and Pedrycz 2002, 2003, 2005Apolloni etal. 2008;Srivastava et al 1999;Slȩzak 2009;Pedrycz and Song 2011;Qian et al 2011) plays a pivotal role and becomes of paramount importance, both from the conceptual as well as algorithmic perspective to the realization of granular models of time series.…”

Section: Introductionmentioning

confidence: 99%

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Human-centric analysis and interpretation of time series: a perspective of granular computing

Pedrycz

Liu

et al. 2014

Soft Comput

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In spite of the truly remarkable diversity of models of time series, there is still an evident need to develop constructs whose accuracy and interpretability are carefully identified and reconciled subsequently leading to highly interpretable (human-centric) constructs. While a great deal of research has been devoted to the design of nonlinear numeric models of time series (with an evident objective to achieve high accuracy of prediction), an issue of interpretability (transparency) of models of time series becomes an evident and ongoing challenge. The user-friendliness of models of time series comes with an ability of humans to perceive and process abstract constructs rather than dealing with plain numeric entities. In perception of time series, information granules (which are regarded as realizations of interpretable entities) play a pivotal role. This gives rise to a concept of granular models of time series or granular time series, in brief. This study revisits generic concepts of information granules and elaborates on a fundamental way of forming information granules (both sets-intervals as well as fuzzy sets) through applying a principle of justifiable granularity encountered in granular computing. Information granules are discussed with regard to the granulation of time series in a certain predefined representation space (viz. a feature space) and granulation carried out in time. The granular representation and description of time series is then presented. We elaborate on the fundamental hierarchically organized layers of processing supporting the development and interpretation of granular time series, namely (a) formation of granular descriptors used in their visualization, (b) construction of linguistic descriptors used afterwards in the generation of (c) linguistic description of time series. The layer of the linguistic prediction models of time series exploiting the linguistic descriptors is outlined as well. A number of examples are offered throughout the entire paper with intent to illustrate the main functionalities of the essential layers of the granular models of time series.

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Section: Experimental Data Sets-a Summarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human-centric analysis and interpretation of time series: a perspective of granular computing

Pedrycz

Liu

et al. 2014

Soft Comput

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“…However, our method has an extra component that dynamically determines the number of segments (see Section 2.2). Another similar work using a different approach to determine the number of segments is reported in (Abonyi et al, 2005). Figure 9-2 is an example of a well log with 189 data points, which are partitioned into 10 segments.…”

Section: Segmentationmentioning

confidence: 99%

A Co-Evolutionary Fuzzy System for Reservoir Well Logs Interpretation

Wilkinson

2008

Evolutionary Computation in Practice

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Well log data are routinely used for stratigraphic interpretation of the earth's subsurface. This paper investigates using a co-evolutionary fuzzy system to generate a well log interpreter that can automatically process well log data and interpret reservoir permeability. The methodology consists of 3 steps: 1) transform well log data into fuzzy symbols which maintain the character of the original log curves; 2) apply a co-evolutionary fuzzy system to generate a fuzzy rule set that classifies permeability ranges; 3) use the fuzzy rule set to interpret well logs and infer the permeability ranges. We present the developed techniques and test them on well log data collected from oil fields in offshore West Africa. The generated fuzzy rules give sensible interpretation. This result is encouraging in two respects. It indicates that the developed well log transformation method preserves the information required for reservoir properties interpretation. It also suggests that the developed co-evolutionary fuzzy system can be applied to generate well log interpreters for other reservoir properties, such as lithology.

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“…Ref. [25] proposed the modified Gath-Geva clustering algorithm for fuzzy segmentation of multivariate time series. The algorithm uses local probabilistic principal component analysis (PPCA) models to measure the homogeneity of the segments and fuzzy sets to represent segments in time, which is able to detect changes in the hidden structure of multivariate time-series [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…Based on [25], the improved Gath-Geva (IGG) clustering algorithm is proposed for automatic fuzzy segmentation of univariate and multivariate time series [28]. The algorithm considers time series segmentation problem as Gath-Geva clustering and the minimum message length criterion [29] is used as segmentation order selection criterion.…”

Section: Introductionmentioning

confidence: 99%

An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation

Yin¹,

Wang²

2015

Proceedings in Adaptation, Learning and Optimization

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a b s t r a c tA novel sampling pool selection scheme is proposed for the online sequential extreme learning machine (OS-ELM) based on improved Gath-Geva (IGG) fuzzy segmentation algorithm. Tidal change is a timevarying process whose dynamics vary with changes of internal and environmental factors such as celestial bodies movements, coastal topology and environmental disturbances. When OS-ELM is implemented for identifying time-varying system dynamics, it usually sequentially selects samples with fixed number. Under such circumstance, samples representing different system dynamics are mixed together so that the online representation and prediction abilities of OS-ELM may be deteriorated. To consciously select samples with most representing ability and construct appropriate sampling pool for OS-ELM, in this study, a dynamic sampling pool selection scheme is proposed based on IGG fuzzy segmentation approach. Time series of input and output variables are segmented as per their dynamics characteristics. The change points split up the time series into several segments and the change points themselves represent the changes of system dynamics. Samples within the same segment are considered as possessing homogeneous characteristics. To achieve best representing abilities for current system dynamics, the proposed IGG-based sampling scheme is implemented for selecting sampling pool. The OS-ELM selects homogeneous samples from sampling pool thus possesses better representing ability for current dynamics. In the meantime, conventional harmonic analysis is also applied to represent the influences of celestial bodies and coastal topology. The harmonic method and IGG-based OS-ELM are combined together and the resulted modular prediction scheme is applied for online tidal level prediction of ports of King Point, Mokuoloe and Old Port Tampa in the United States. Simulation results demonstrate the feasibility and effectiveness of the proposed sampling scheme and the modular tidal prediction approach.

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Modified Gath–Geva clustering for fuzzy segmentation of multivariate time-series

Cited by 116 publications

References 21 publications

Human-centric analysis and interpretation of time series: a perspective of granular computing

Human-centric analysis and interpretation of time series: a perspective of granular computing

A Co-Evolutionary Fuzzy System for Reservoir Well Logs Interpretation

An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation

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