VDBSCAN: Varied Density Based Spatial Clustering of Applications with Noise

Liu, Peng; Zhou, Dong; Wu, Nanping

doi:10.1109/icsssm.2007.4280175

Cited by 156 publications

(80 citation statements)

References 3 publications

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“…An added advantage is that the sensitivity of the input parameter ε, which is an important disadvantage of DBSCAN, is reduced significantly. In VDBSCAN [9] the author has also tried to improve the result using DBSCAN algorithm. The method computes k-distance for each object and sort them in ascending order, then plotted using the sorted values.…”

Section: Related Workmentioning

confidence: 99%

A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

Ram¹,

Jalal²,

Jalal³

et al. 2010

IJCA

149

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DBSCAN is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluster. It calculates the growing cluster density mean and then the cluster density variance for any core object, which is supposed to be expended further, by considering density of its -neighborhood with respect to cluster density mean. If cluster density variance for a core object is less than or equal to a threshold value and also satisfying the cluster similarity index, then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results. General TermsDensity Based Clustering

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Section: Related Workmentioning

confidence: 99%

A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

Ram¹,

Jalal²,

Jalal³

et al. 2010

IJCA

149

View full text Add to dashboard Cite

show abstract

“…Examples of density based clustering algorithms are OPTICS, DENCLUE, CLIQUE, and DBSCAN [1,3,7]. DBSCAN algorithm has been inspiration of several studies since it was first proposed due to its potential to discover clusters with different shapes and sizes in noisy data [1][2][3][4][5][6][7][8][9][10]. In contrast to its popularity, determination of input parameters of DBSCAN is challenging.…”

Section: Related Workmentioning

confidence: 99%

A New Approach to Determine Eps Parameter of DBSCAN Algorithm

Ozkok

Çelik

2017

ijisae

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Abstract:In recent years, data analysis has become important with increasing data volume. Clustering, which groups objects according to their similarity, has an important role in data analysis. DBSCAN is one of the most effective and popular density-based clustering algorithm and has been successfully implemented in many areas. However, it is a challenging task to determine the input parameter values of DBSCAN algorithm which are neighborhood radius Eps and minimum number of points MinPts. The values of these parameters significantly affect clustering performance of the algorithm. In this study, we propose AE-DBSCAN algorithm which includes a new method to determine the value of neighborhood radius Eps automatically. The experimental evaluations showed that the proposed method outperformed the classical method.

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“…Darmon et al (2014) chose the Order Statistics Local Optimization Method, or OSLOM, (Lancichinetti et al, 2011) for community detection because of its ability to work with weighted and directed graphs, and its ability to identify overlapping communities. Bakillah et al (2015) chose the Fast-Greedy Optimization of Modularity, or FGM, (Clauset et al, 2004) for its ability to handle complex social graphs from Twitter, and the Varied Density-Based Spatial Clustering of Applications with Noise, or VDBSCAN, (Liu et al, 2007) for its ability to obtain spatial clusters at certain points in time.…”

Section: Algorithmsmentioning

confidence: 99%

Improving Twitter Community Detection through Contextual Sentiment Analysis

Lam

2016

Proceedings of the ACL 2016 Student Research Workshop

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Works on Twitter community detection have yielded new ways to extract valuable insights from social media. Through this technique, Twitter users can be grouped into different types of communities such as those who have the same interests, those who interact a lot, or those who have similar sentiments about certain topics. Computationally, information is represented as a graph, and community detection is the problem of partitioning the graph such that each community is more densely connected to each other than to the rest of the network. It has been shown that incorporating sentiment analysis can improve community detection when looking for sentiment-based communities. However, such works only perform sentiment analysis in isolation without considering the tweet's various contextual information. Examples of these contextual information are social network structure, and conversational, author, and topic contexts. Disregarding these information poses a problem because at times, context is needed to clearly infer the sentiment of a tweet. Thus, this research aims to improve detection of sentiment-based communities on Twitter by performing contextual sentiment analysis.

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VDBSCAN: Varied Density Based Spatial Clustering of Applications with Noise

Cited by 156 publications

References 3 publications

A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

A New Approach to Determine Eps Parameter of DBSCAN Algorithm

Improving Twitter Community Detection through Contextual Sentiment Analysis

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