A Fuzzy Clustering Model for Multivariate Spatial Time Series

Coppi, Renato; D’Urso, Pierpaolo; Giordani, Paolo

doi:10.1007/s00357-010-9043-y

Cited by 85 publications

(82 citation statements)

References 46 publications

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“…Other examples of clustering for three-way data arrays can be found in, for instance, Gordon and Vichi (2001), Vichi, Rocci, and Kiers (2007), Coppi, d'Urso, and Giordani (2010).…”

Section: Remarkmentioning

confidence: 98%

FINDCLUS: Fuzzy INdividual Differences CLUStering

Giordani

Kiers

2012

J Classif

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ADditive CLUStering (ADCLUS) is a tool for overlapping clustering of two-way proximity matrices (objects objects). In Simple Additive Fuzzy Clustering (SAFC), a variant of ADCLUS is introduced providing a fuzzy partition of the objects, that is the objects belong to the clusters with the so-called membership degrees ranging from zero (complete non-membership) to one (complete membership). INDCLUS (INdividual Differences CLUStering) is a generalization of ADCLUS for handling three-way proximity arrays (objects  objects  subjects). Here, we propose a fuzzified alternative to INDCLUS capable to offer a fuzzy partition of the objects by generalizing in a three-way context the idea behind SAFC. This new model is called Fuzzy INdividual Differences CLUStering (FINDCLUS). An algorithm is provided for fitting the FINDCLUS model to the data. Finally, the results of a simulation experiment and some applications to synthetic and real data are discussed.

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“…Other examples of clustering for three-way data arrays can be found in, for instance, Gordon and Vichi (2001), Vichi, Rocci, and Kiers (2007), Coppi, d'Urso, and Giordani (2010).…”

Section: Remarkmentioning

confidence: 98%

FINDCLUS: Fuzzy INdividual Differences CLUStering

Giordani

Kiers

2012

J Classif

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“…It should be remarked that, in contrast to the Kruse and Meyer's approach, statistical conclusions with Puri and Ralescu random fuzzy sets always concern the fuzzy-valued random element and the parameters associated with its induced distribution. An interesting distinctive feature of the statistical methodology based on this approach to generate fuzzy data is that most of the classical ideas in data analysis can be immediately preserved without needing to either define or adapt Huang and Ng (1999) and Lee and Pedrycz (2009) Functional data Tokushige et al (2007) and Tan et al (2013) Textual data (text data) Runkler and Bezdek (2003) Time data Coppi and D'Urso (2002, 2003, D'Urso (2005), Maharaj and D'Urso (2011, 2016, 2017b Spatial data Pham (2001) Spatial-time data Coppi et al (2010) and Disegna et al (2017) Three-way data Giordani (2010) and Rocci and Vichi (2005) Sequence data D'Urso and Massari (2013) Network data Liu (2010) Directional data Yang and Pan (1997) and Kesemen et al (2016) Distributional data Irpino et al (2017) Mixed data Yang et al (2004) Outlier data Davé (1991), Krishnapuram and Keller (1993), Frigui and Krishnapuram (1996), Wu and Yang (2002), D'Urso and Giordani (2006), Fritz et al (2013), Ferraro and Vichi (2015), Ferraro and Giordani (2017), D'Urso et al (2015aD'Urso et al ( , b, 2016D'Urso et al ( , 2017a, D'Urso and Leski (2016) and Yang and Nataliani (2017) Incomplete data …”

Section: On the Analysis And Classification Of Fuzzy Datamentioning

confidence: 99%

Fuzzy data analysis and classification

D’Urso

Alvarez

2017

Adv Data Anal Classif

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In analyzing and classifying data from a statistical perspective, fuzzy sets and logic have become a valuable tool either to model and handle imprecise data or to establish flexible techniques to deal with precise data.From the very beginning of his 52 years-old theory, Professor Zadeh highlighted that "Probability theory/statistics and fuzzy logic should be viewed as complementary rather than competitive," and he anticipated and encouraged the materialization of such a complementarity. Nowadays, this assertion is a reality, as shown by the many related papers, specialized conferences, special sessions and tracks in general conferences, and so on.This special issue started in 2015, with the 50th anniversary of the seminal paper on fuzzy sets by Zadeh (1965), aiming to collect a sample of research papers about the current trends on the combination of fuzzy sets/logic and data analysis/classification. When this special issue was almost ready for publication, Zadeh unfortunately passed away at age 96 (February 4, 1921-September 6, 2017. We wish this special issue to be dedicated to Professor Zadeh, as a modest part of the many tributes that he will receive, and intending to show that fuzzy sets/logic and data analysis/classification can certainly work in synergy.

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“…Trajectory patterns (Chen et al 2005;Gudmundsson et al 2012;Kang and Yong 2009) of moving objects can provide useful information for high quality location-based services (LBS), such as traffic flow control or location-aware advertising, etc. Clustering trajectory data (Coppi and D'Urso 2003;Coppi et al 2010;D'Urso 2005D'Urso , 2004Ester et al 1998;Gaffney and Smyth 1999;Zhang et al 1996;Zhou et al 2010) to reveal interesting correlations is an important technique for vehicle traffic management. This paper precisely addresses the problem of identifying the traffic flow patterns in bi-directional road network.…”

Section: Introductionmentioning

confidence: 99%

Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering

Mirge

Verma

Gupta

2016

Adv Data Anal Classif

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Due to the rapid growth of wireless communications and positioning technologies, trajectory data have become increasingly popular, posing great challenges to the researchers of data mining and machine learning community. Trajectory data are obtained using GPS devices that capture the position of an object at specific time intervals. These enormous amounts of data necessitates to explore efficient and effective techniques to extract useful information to solve real world problems. Traffic flow pattern mining is one of the challenging issues for many applications. In a literature significant number of approaches are available to cluster the trajectory data, however the clustering has not been explored for trajectories pattern mining in bi-directional road networks. This paper presents a novel technique for excavating heavy traffic flow patterns in bi-directional road network, i.e. identifying divisions of the roads where the traffic flow is very dense. The proposed technique works in two phases: phase I, finds the clusters of trajectory points based on density of trajectory points; phase II, arranges the clusters in sequence based on spatiotemporal values for each route and directions. These sequences represent the traffic flow patterns. All the routes and sections exceeding a user specified minimum traffic threshold are marked as high dense traffic areas. The experiments are performed on synthetic dataset. The proposed algorithm efficiently and accurately finds the dense traffic in bi-directional roads. Proposed B Vaishali Mirge

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A Fuzzy Clustering Model for Multivariate Spatial Time Series

Abstract: Array of space time data, Fuzzy clustering, Spatial autocorrelation function, Spatial penalization term, Dissimilarity measures between multivariate time trajectories,

Cited by 85 publications

References 46 publications

FINDCLUS: Fuzzy INdividual Differences CLUStering

FINDCLUS: Fuzzy INdividual Differences CLUStering

Fuzzy data analysis and classification

Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering

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