Spatial Entropy-Based Clustering for Mining Data with Spatial Correlation

Wang, Baijie; Wang, Xin

doi:10.1007/978-3-642-20841-6_17

Cited by 7 publications

(11 citation statements)

References 8 publications

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“…Smart planet is based on the digital earth. Its neural network is the Internet of Things, and its thinking is implemented via cloud computing . The Internet of Things integrated human society and physical system, i.e., spatial data prompted the fusion of digital infrastructure and physical infrastructure.…”

Section: Spatial Datamentioning

confidence: 99%

“…For example, the context‐related conflicts from different systems and applications may cause inconsistent data, omissions lead to incomplete data, and the same object with a different spectrum (or different object with the same spectrum) may result in uncertain image classification. Furthermore, these data are often affected by gross errors, system errors, and random errors individually or collaboratively . The Massachusetts Institute of Technology found that most of the data accuracy is less than 95% .…”

Section: Spatial Datamentioning

confidence: 99%

“…These spatial data are voluminous and grow rapidly, which have far exceeded the human ability to timely interpret and use with common techniques . For example, a newspaper published the same article of the same author on two different pages of ‘legal community’ and ‘youth topics.’ Another newspaper published three articles in the edition of ‘home appliances,’ ‘lifestyle,’ and ‘science and technology,’ all to compare video compact disc (VCD), China video disc (CVD), and digital versatile disc (DVD) on the same day and obtained three different conclusions, but the editor did not even find it .…”

Section: Introductionmentioning

confidence: 99%

“…Traditional theory and method of RS image processing can process a very limited amount of data in a low level of automation and intelligence. Most of the results stay on the level of qualitative . Commercial image processing software (ERDAS, IMAGINE, PCI, ENVI, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…SDM can be applied anywhere there are spatial data because they are fundamental in data‐referenced decision making . The discovered knowledge may improve the level of resource productivity and utilization in the context of sustainable development and benefit the response to economic crisis, energy crisis, environment protection, and other global issues . It also helps people to learn, work, live, and achieve the wisdom state in a much more precise and dynamic way .…”

Section: Introductionmentioning

confidence: 99%

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Software and applications of spatial data mining

Wang

Yuan

et al. 2016

WIREs Data Min & Knowl

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Most big data are spatially referenced, and spatial data mining (SDM) is the key to the value of big data. In this paper, SDM are overviewed in the aspects of software and application. First, spatial data are summarized on their rapid growth, distinct characteristics, and implicit values. Second, the principles of SDM are briefed with the descriptive definition, fundamental attributes, discovery mechanism, and usable methods. Third, SDM software is presented in the context of software components, developing methodology, typical software for geographical information system (GIS) data and remote sensing (RS) images, and software trend. Fourth, SDM applications are outlined on GIS data, RS image, and spatiotemporal video data. The final is the concluding remarks and perspectives.

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Section: Spatial Datamentioning

confidence: 99%

Section: Spatial Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Software and applications of spatial data mining

Wang

Yuan

et al. 2016

WIREs Data Min & Knowl

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The big data of violent events: algorithms for association analysis using spatio-temporal storytelling

et al. 2016

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An Improved Density-Based Approach to Spatio-Textual Clustering on Social Media

Nguyen

Shin

2019

IEEE Access

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Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm but may not be sufficient when the input data type is heterogeneous in terms of textual description. When we aim to discover clusters of geo-tagged records relevant to a particular point of interest (POI) on social media, examining only one type of input data (e.g., the tweets relevant to a POI) may draw an incomplete picture of clusters due to noisy regions. To overcome this problem, we introduce DBSTexC, a newly defined density-based clustering algorithm using spatio-textual information on social media (e.g., Twitter). We first characterize the POI-relevant and POI-irrelevant geotagged tweets as the texts that include and do not include a POI name or its semantically coherent variations, respectively. By leveraging the proportion of the POI-relevant and POI-irrelevant tweets, the proposed algorithm demonstrates much higher clustering performance than the DBSCAN case in terms of F 1 score and its variants. While DBSTexC performs exactly as DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with the textually heterogeneous inputs. Furthermore, to further improve the clustering quality by fully capturing the geographic distribution of geo-tagged points, we present fuzzy DBSTexC (F-DBSTexC), an extension of DBSTexC, which incorporates the notion of fuzzy clustering into the DBSTexC. We then demonstrate the consistent superiority of F-DBSTexC over the original DBSTexC via intensive experiments. The computational complexity of our algorithms is also analytically and numerically shown. INDEX TERMS Density-based clustering, fuzzy clustering, geo-tagged record, point-of-interest (POI), spatio-textual information. I. INTRODUCTION A. BACKGROUND

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Spatial Entropy-Based Clustering for Mining Data with Spatial Correlation

Cited by 7 publications

References 8 publications

Software and applications of spatial data mining

Software and applications of spatial data mining

The big data of violent events: algorithms for association analysis using spatio-temporal storytelling

An Improved Density-Based Approach to Spatio-Textual Clustering on Social Media

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