A clustering algorithm for mobile ad hoc networks based on spatial auto-correlation

Jahani, Samaneh; Bagherpour, Matin

doi:10.1109/cnds.2011.5764560

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…The consideration of spatial autocorrelation in clustering has been already investigated in the literature (Glotsos et al, 2004;Jahani and Bagherpour, 2011). Motivated by the demonstrated benefits of considering autocorrelation, in this paper, we exploit some characteristics of autocorrelated data to improve the quality of PCTs.…”

Section: Introductionmentioning

confidence: 99%

Dealing with spatial autocorrelation when learning predictive clustering trees

Stojanova

Ceci

Appice

et al. 2013

Ecological Informatics

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Spatial autocorrelation is the correlation among data values which is strictly due to the relative spatial proximity of the objects that the data refer to. Inappropriate treatment of data with spatial dependencies, where spatial autocorrelation is ignored, can obfuscate important insights. In this paper, we propose a data mining method that explicitly considers spatial autocorrelation in the values of the response (target) variable when learning predictive clustering models. The method is based on the concept of predictive clustering trees (PCTs), according to which hierarchies of clusters of similar data are identified and a predictive model is associated to each cluster. In particular, our approach is able to learn predictive models for both a continuous response (regression task) and a discrete response (classification task). We evaluate our approach on several real world problems of spatial regression and spatial classification. The consideration of the autocorrelation in the models improves predictions that are consistently clustered in space and that clusters try to preserve the spatial arrangement of the data, at the same time providing a multi-level insight into the spatial autocorrelation phenomenon. The evaluation of SCLUS in several ecological domains (e.g. predicting outcrossing rates within a conventional field due to the surrounding genetically modified fields, as well as predicting pollen dispersal rates from two lines of plants) confirms its capability of building spatial aware models which capture the spatial distribution of the target variable. In general, the maps obtained by using SCLUS do not require further post-smoothing of the results if we want to use them in practice.

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Section: Introductionmentioning

confidence: 99%

Dealing with spatial autocorrelation when learning predictive clustering trees

Stojanova

Ceci

Appice

et al. 2013

Ecological Informatics

View full text Add to dashboard Cite

show abstract

“…The consideration of autocorrelation in clustering has been the subject of some recent work in spatial clustering (Glotsos et al 2004) and network clustering (Jahani and Bagherpour 2011). Motivated by the demonstrated benefits of autocorrelation, we exploit some properties of autocorrelation to improve the quality of the PCTs.…”

Section: Exploiting the Properties Of Autocorrelation In Nclusmentioning

confidence: 97%

Network regression with predictive clustering trees

Stojanova

Ceci

Appice

et al. 2012

Data Min Knowl Disc

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Network data describe entities represented by nodes, which may be connected with (related to) each other by edges.Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, calledNCLUS, that explicitly considers autocorrelationwhen building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multitarget prediction, including multi-target regression and multi-target classification.We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results showthat our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approache

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“…Considering spatial autocorrelation in spatial analysis, and specifically clustering, is important as it results in extracting more stable clusters (Glotsos et al 2004, Jahani andBagherpour 2011). Global spatial autocorrelation evaluates whether the existing pattern over the study area is clustered, random, or dispersed.…”

Section: Spatial Autocorrelationmentioning

confidence: 99%

Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

Ghaemi

Farnaghi

2021

Journal of Spatial Science

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Twitter, as the most popular social media platform, has made a great revolution in producing real-time user-generated data. This research aims to propose a method to extract the latent spatial pattern from geotagged tweets. We take both spatial autocorrelation and spatial heterogeneity into account while revealing the underlying pattern from geotagged tweets. Moreover, the textual similarity is considered to extract spatial-textual clusters. The method was implemented and tested during hurricane Dorian on the east coast of the U.S. The results proved the superiority of the proposed method against Moran's Index and VDBSCAN algorithms in extracting clusters with various densities.

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A clustering algorithm for mobile ad hoc networks based on spatial auto-correlation

Cited by 6 publications

References 18 publications

Dealing with spatial autocorrelation when learning predictive clustering trees

Dealing with spatial autocorrelation when learning predictive clustering trees

Network regression with predictive clustering trees

Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

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