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Footprint generation using fuzzy-neighborhood clustering
2012
Geoinformatica
Preserved Fulltext
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
2007
Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07
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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]
2014
Communications in Computer and Information Science
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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
GIS-BASED MAPPING OF LOCAL CLIMATE ZONES USING FUZZY LOGIC AND CELLULAR AUTOMATA
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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]
2021
arXiv
pre-print
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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
2019
Traitement du signal
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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
2012
Engineering Journal
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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
2010
IEEE Transactions on Geoscience and Remote Sensing
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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
2019
ISPRS International Journal of Geo-Information
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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]
2019
arXiv
pre-print
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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
2021
Land
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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]
2017
arXiv
pre-print
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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
2020
Sustainability
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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
2021
Algorithms
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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
FREE SHAPE CONTEXT DESCRIPTORS OPTIMIZED WITH GENETIC ALGORITHM FOR THE DETECTION OF DEAD TREE TRUNKS IN ALS POINT CLOUDS
2015
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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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