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Footprint generation using fuzzy-neighborhood clustering

Jonathon K. Parker, Joni A. Downs
2012 Geoinformatica  
Footprint generation using fuzzy-neighborhood clustering. GeoInformatica, DOI: 10.1007/s10707-012-0152-0. 19 Joni A. Downs and Mark W. Horner.  ...  Analysing infrequently sampled animal tracking data by incorporating generalized movement trajectories with kernel density estimation. 2012.  ... 
doi:10.1007/s10707-012-0152-0 fatcat:gxatut7bmbahrj7brfnaq53iwa

Neighborhood restrictions in geographic IR

Steven Schockaert, Martine De Cock
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
In this paper, we propose such a technique, using fuzzy footprints to cope with the inherent vagueness of most neighborhood boundaries, and we provide experimental results that demonstrate the potential  ...  Unfortunately, many useful types of geographic restrictions are currently not supported in these systems, including restrictions that specify the neighborhood in which the business should be located.  ...  Neighborhood boundaries are generally considered to be inherently fuzzy [24] .  ... 
doi:10.1145/1277741.1277772 dblp:conf/sigir/SchockaertC07 fatcat:ektazczalre2xfcknbjr4r263u

Fuzzy Core DBScan Clustering Algorithm [chapter]

Gloria Bordogna, Dino Ienco
2014 Communications in Computer and Information Science  
In this work we propose an extension of the DBSCAN algorithm to generate clusters with fuzzy density characteristics.  ...  In order to deal with this issue, we propose Approx Fuzzy Core DBSCAN that applies a soft constraint to model different densities, thus relaxing the rigid assumption used in the original algorithm.  ...  Having fuzzy clusters allows generating isolines footprints. An efficient implementation is proposed in [2] .  ... 
doi:10.1007/978-3-319-08852-5_11 fatcat:q5m3ad6pubg3rjjwzemeaegepy


I. Estacio, J. Babaan, N. J. Pecson, A. C. Blanco, J. E. Escoto, C. K. Alcantara
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The developed methodology and tool is ready to be used in other cities as long as the input layers are generated.  ...  Fuzzy Logic was used to determine the membership percentage of 100 m cells to an LCZ type based on these properties.  ...  Clusters large enough to be an LCZ was detected using a 5x5 square neighborhood window.  ... 
doi:10.5194/isprs-archives-xlii-4-w19-199-2019 fatcat:azw32vhbrnghpiqzrw7a3aa4ea

StreamSoNG: A Soft Streaming Classification Approach [article]

Wenlong Wu, James M. Keller, Jeffrey Dale, James C. Bezdek
2021 arXiv   pre-print
In this paper, we propose a new streaming classification algorithm that uses Neural Gas prototypes as footprints and produces a possibilistic label vector (of typicalities) for each incoming vector.  ...  Examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models.  ...  The footprints can additionally contain the structure of fuzzy rules [20] , [21] . In [22] , an evolving system was proposed that could add, remove, merge and split clusters.  ... 
arXiv:2010.00635v2 fatcat:ewcckxnhyvfbdg6ymsev3nzbxe

A Footprint Extraction and Recognition Algorithm Based on Plantar Pressure

Chao Zhang, Su Pan, Yaowu Qi, Yudan Yang
2019 Traitement du signal  
Then, an 8neighborhood connected-component labeling algorithm was proposed to segment and cluster the plantar pressure images.  ...  Finally, the footprints were recognized based on the plantar pressure and shape of footprints.  ...  Then, the start and end timestamps of the footprints in each step were compared, using the 8-neighborhood connectedcomponent labeling algorithm.  ... 
doi:10.18280/ts.360506 fatcat:4iffaoyg4fbwtkoszy7dbfpur4

Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach

Murugeswari Palanivel, Manimegalai Duraisamy
2012 Engineering Journal  
Then, Extended Interval Type-2 Fuzzy C-means clustering algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the textured image.  ...  This paper presents the hybrid approach for color texture segmentation using Haralick features extracted from the Integrated Color and Intensity Co-occurrence Matrix.  ...  Acknowledgment The authors would like to acknowledge the Tijuana Institute of Technology and Baja California Autonomous University, Tijuana Campus, Mexico for providing the Interval Type-2 Fuzzy toolbox  ... 
doi:10.4186/ej.2012.16.5.115 fatcat:cpurbbmmizhmnh2cubiiffy72m

Segmentation and Reconstruction of Polyhedral Building Roofs From Aerial Lidar Point Clouds

A. Sampath, Jie Shan
2010 IEEE Transactions on Geoscience and Remote Sensing  
In the second step, the surface normals of all planar points are clustered with the fuzzy k-means method.  ...  To optimize this clustering process, a potential-based approach is used to estimate the number of clusters, while considering both geometry and topology for the cluster similarity.  ...  The cluster centers generated for each r a are used as the input to the fuzzy k-means clustering algorithm.  ... 
doi:10.1109/tgrs.2009.2030180 fatcat:dpwrah2t7rhihbgszgoruk7owa

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Zhanlong Chen, Xiaochuan Ma, Liang Wu, Zhong Xie
2019 ISPRS International Journal of Geo-Information  
Finally, the similarity graph containing the neighborhood relationship between polygons is acquired, allowing for clustering using the proposed adjacency graph model.  ...  In this study, we propose a novel fuzzy similarity approach for spatial clustering, called Extend Intuitionistic Fuzzy Set-Interpolation Boolean Algebra (EIFS-IBA).  ...  D connectivity (C) were used to describe the neighborhood relationship of polygons.  ... 
doi:10.3390/ijgi8020098 fatcat:xd3snh4rzrhrhfpokhascpbdly

FISHDBC: Flexible, Incremental, Scalable, Hierarchical Density-Based Clustering for Arbitrary Data and Distance [article]

Matteo Dell'Amico
2019 arXiv   pre-print
It is hierarchical: it produces a "flat" clustering which can be expanded to a tree structure, so that users can group and/or divide clusters in sub- or super-clusters when data exploration requires so  ...  FISHDBC is a flexible, incremental, scalable, and hierarchical density-based clustering algorithm.  ...  its neighborhood is considered to be in the same cluster.  ... 
arXiv:1910.07283v1 fatcat:h3ilyga5yrfqhifll35f5wmxde

Spatially Explicit Fuzzy Cognitive Mapping for Participatory Modeling of Stormwater Management

Corey T. White, Helena Mitasova, Todd K. BenDor, Kevin Foy, Okan Pala, Jelena Vukomanovic, Ross K. Meentemeyer
2021 Land  
We found that stakeholders used a wide variety of language to describe variables in their FCMs and that government and academic stakeholders used significantly different suites of variables.  ...  Fuzzy cognitive maps (FCMs) are participatory modeling tools that enable diverse stakeholders to articulate the components of a socio-environmental system (SES) and describe their interactions.  ...  To explore how the development in each cluster's upstream contributing area changed over time, we used building footprint data to generate a map depicting the decade in which each building was developed  ... 
doi:10.3390/land10111114 fatcat:e7q64oe3afcajk4247wluigfla

PANFIS++: A Generalized Approach to Evolving Learning [article]

Mahardhika Pratama
2017 arXiv   pre-print
This module allows to actively select data streams for the training process, thereby expediting execution time and enhancing generalization performance, 2) PANFIS++ is built upon an interval type-2 fuzzy  ...  system environment, which incorporates the so-called footprint of uncertainty.  ...  its fuzzy rules in the single-pass learning mode using the Generalized Type-2 Datum Significance (GT2DS) method.  ... 
arXiv:1705.02476v1 fatcat:wkwjlafrkfdsvlf62rslqviqqe

People and the City: Urban Fragility and the Real Estate-Scape in a Neighborhood of Catania, Italy

Maria Rosa Trovato, Claudia Clienti, Salvatore Giuffrida
2020 Sustainability  
Fuzzy k-medoids cluster analyses have been comparatively performed—showing and mapping the relationships between urban value density and real estate market prices tensions.  ...  This analysis was performed by implementing a multidimensional pattern allowing us to place the neighborhood in a ranking of the neighborhoods of Catania, thus highlighting strength and weakness under  ...  Therefore, the analysis aimed at identifying the submarkets of the neighborhood was conducted by means fuzzy clustering that generalizes partition clustering method k-medoids.  ... 
doi:10.3390/su12135409 fatcat:v35poibcwbaw5gdsl2c7m7ioda

Towards Understanding Clustering Problems and Algorithms: An Instance Space Analysis

Luiz Henrique dos Santos Fernandes, Ana Carolina Lorena, Kate Smith-Miles
2021 Algorithms  
Various criteria and algorithms can be used for clustering, leading to very distinct outcomes and potential biases towards datasets with certain structures.  ...  More generally, the selection of the most effective algorithm to be applied for a given dataset, based on its characteristics, is a problem that has been largely studied in the field of meta-learning.  ...  [5] , the algorithm that generates the footprints uses a combination of two approaches that were used in previous works.  ... 
doi:10.3390/a14030095 fatcat:fl6rjheavraorf4hh4qlgxlto4


P. Polewski, W. Yao, M. Heurich, P. Krzystek, U. Stilla
2015 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper, a new family of shape descriptors called Free Shape Contexts (FSC) is introduced to generalize the existing 3D Shape Contexts.  ...  For this purpose, a fuzzy logic controller (FLC) which dynamically adjusts the magnitude of the recombination effects is co-evolved with the FSC parameters in a two-tier evolution scheme.  ...  It can be carried out using any appropriate clustering method. In particular, we use the Normalized-Cut based approach by Reitberger et al. (2009) .  ... 
doi:10.5194/isprsannals-ii-3-w5-41-2015 fatcat:ccsujkms4neo7iws4csia5ndrm
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