Visual Analytics for Spatial Clustering: Using a Heuristic Approach for Guided Exploration

Packer, Eli; Bak, Peter; Nikkilä, Mikko; Polishchuk, Valentin; Ship, Harold J.

doi:10.1109/tvcg.2013.224

Cited by 24 publications

(11 citation statements)

References 31 publications

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“…The basic assumption made by the authors of the papers in this category is that an interactive and collaborative process combining the strengths of both human and machine would yield better results than a process that is purely automated or purely manual. Several examples of improving the quality of the clustering results using different strategies are given in the works presented in Andrienko and Andrienko [4], Basu et al [15], Boudjeloud-Assala et al [19], Cao et al [24], Castellanos-Garzón et al [26], Choo et al [30], Dobrynin et al [38], Hadlak et al [50], Hoque and Carenini [53], Hu et al [55], Kumpf et al [64], Lai et al [66], Lee et al [67], Lei et al [68], MacInnes et al [72], Packer et al [79], Schreck et al [86], Srivastava et al [94], Turkay et al [99,101], Zhou et al [116].…”

Section: Improving the Clustering Qualitymentioning

confidence: 99%

“…We find that most of the approaches support updating the usual parameters of the clustering methods, e.g., adjust the number of clusters or the similarity threshold parameters [5,8,19,38,43,45,49,62,65,68,70,72,79,[92][93][94]101]. A somewhat unique perspective in this category is provided by Xiao and Dunham [111], where the authors note that when the whole dataset is unknown a priori, choosing an appropriate value of the number of clusters may be difficult.…”

Section: Interacting With the Model's Parametersmentioning

confidence: 99%

“…In many cases, the methods allow interactions with both the model and the results [7,15,19,30,32,55,79,86,94,113]. For example, users can force desired meanings for a cluster by manipulating the weights assigned to particular terms within different topics, but they can also delete, merge, move, re-cluster, and sub-cluster the results.…”

Section: Interacting With the Model's Parametersmentioning

confidence: 99%

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Interactive Clustering

et al. 2020

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In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

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Section: Improving the Clustering Qualitymentioning

confidence: 99%

Section: Interacting With the Model's Parametersmentioning

confidence: 99%

Section: Interacting With the Model's Parametersmentioning

confidence: 99%

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Interactive Clustering

et al. 2020

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“…It is the fact that different users may have different analysis targets of business objectives [1]. Then, when we evaluate the result of the clustering algorithm, we also want to know whether the result can reflect the user's intent or not [2]. …”

Section: Introductionmentioning

confidence: 99%

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine

Yang

Dai

Bin

et al. 2017

Computational and Mathematical Methods in Medicine

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Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K-means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

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“…Researchers pay great attention towards clustering such spatially distributed data in an efficient manner [8]. Few spatial clustering algorithms are combined with smoothening filters to make the clustering robust against noise [9] [10] [11], while texture and spectral analysis have also been introduced in other works [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Distance Clustering: A Spatial Clustering Approach for Remote Sensing Imagery

Jayababu¹,

Varma²,

Govardhan³

2015

IJCA

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Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust to handle such peculiar data distribution. This paper employs probabilistic d -clustering algorithm to cluster the geographical information of the remote sensing imagery. The methodology considers diverse neighborhood connectivity and degree of connectivity to investigate the performance of probabilistic d -clustering algorithm. Experimental investigation demonstrates that probabilistic d -clustering algorithm is better than k -means clustering algorithm in handling remote sensing imagery.

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Visual Analytics for Spatial Clustering: Using a Heuristic Approach for Guided Exploration

Cited by 24 publications

References 31 publications

Interactive Clustering

Interactive Clustering

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine

Probabilistic Distance Clustering: A Spatial Clustering Approach for Remote Sensing Imagery

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