An Approach to Find Embedded Clusters Using Density Based Techniques

Roy, Swarup; Bhattacharyya, Dhruba K.

doi:10.1007/11604655_59

Cited by 57 publications

(23 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Roy and Bhattacharyya [23] developed new DBSCAN algorithm, which may help to find different density clusters that overlap. However, the parameters in this method are still defined by users.…”

Section: Related Workmentioning

confidence: 99%

A Vibration Method for Discovering Density Varied Clusters

Elbatta

Bolbol

Ashour

2012

ISRN Artificial Intelligence

View full text Add to dashboard Cite

DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed method VMDBSCAN first finds out the "core" of each cluster-clusters generated after applying DBSCAN. Then, it "vibrates" points toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct number of clusters.

show abstract

Section: Related Workmentioning

confidence: 99%

A Vibration Method for Discovering Density Varied Clusters

Elbatta

Bolbol

Ashour

2012

ISRN Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…Therefore, we discuss two density based approaches, OPTICS [12] and EnDBSCAN [13], which attempt to handle the datasets with variable density successfully. OPTICS can identify embedded clusters over varying density space.…”

Section: Clustering Over Variable Density Spacementioning

confidence: 99%

“…However, its execution time performance degrades in case of large datasets with variable density space and it cannot detect nested cluster structures successfully over massive datasets. In EnDBSCAN [13], an attempt is made to detect embedded or nested clusters using an integrated approach. Based on our experimental analysis in light of very large synthetic datasets, it has been observed that EnDBSCAN can detect embedded clusters; however, with the increase in the volume of data, the performance of it also degrades.…”

Section: Clustering Over Variable Density Spacementioning

confidence: 99%

An Extended Density based Clustering Algorithm for Large Spatial 3D Data using Polyhedron Approach

Barua¹,

Sarmah²

2012

IJCA

View full text Add to dashboard Cite

Discovering the meaningful patterns and trends out of large datasets needs a very special attention now a days, and one of the most prevalent and widely studied problems in this area is the detection and formation of clusters accurately and correctly. Previous works on this field does not meet the problem of 3D spatial datasets with minimization of Input Parameters. The objective of this paper is to present a Tetrahedron-density based clustering technique for large 3D datasets which we have named as 3D-CATD (Three DimensionalClustering Algorithm using Tetrahedron Density), for efficient clustering of 3D spatial data. This algorithm is capable of identifying embedded clusters of arbitrary shapes as well as multi-density clusters over large 3D spatial datasets. The polyhedron approach is being incorporated to perform the clustering where the number of points inside a tetrahedron (tetrahedron density) of a polyhedron is calculated using barycentric formulae for tetrahedron. This is because of the fact that partitioning of the data set can be performed more efficiently in tetrahedron shape than in any other 3D shape due to its smaller space dimension. The ratio of number of points between two tetrahedrons can be found out which forms the basis of nested clustering of 3D data. Experimental results establish the superiority of the technique in terms of cluster quality and complexity.

show abstract

“…MCMC is a strategy for generating samples from virtually any probability distribution, p(x), which is known, is point-wise up to constant; see Robert and Casella (2004) for an introduction. The method generates a Markov chain with a stationary distribution p(x).…”

Section: Metropolis-hastings Algorithmmentioning

confidence: 99%

DECODE: a new method for discovering clusters of different densities in spatial data

Pei

Jasra

Hand

et al. 2008

Data Min Knowl Disc

View full text Add to dashboard Cite

When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of estimating the thresholds in an automatic way, or limited to only two point processes, i.e. noise and clusters with the same density. In this paper, we present a new density-based cluster method (DECODE), in which a spatial data set is presumed to consist of different point processes and clusters with different densities Responsible editor: Charu Aggarwal. belong to different point processes. DECODE is based upon a reversible jump Markov Chain Monte Carlo (MCMC) strategy and divided into three steps. The first step is to map each point in the data to its mth nearest distance, which is referred to as the distance between a point and its mth nearest neighbor. In the second step, classification thresholds are determined via a reversible jump MCMC strategy. In the third step, clusters are formed by spatially connecting the points whose mth nearest distances fall into a particular bin defined by the thresholds. Four experiments, including two simulated data sets and two seismic data sets, are used to evaluate the algorithm. Results on simulated data show that our approach is capable of discovering the clusters automatically. Results on seismic data suggest that the clustered earthquakes, identified by DECODE, either imply the epicenters of forthcoming strong earthquakes or indicate the areas with the most intensive seismicity, this is consistent with the tectonic states and estimated stress distribution in the associated areas. The comparison between DECODE and other state-of-the-art methods, such as DBSCAN, OPTICS and Wavelet Cluster, illustrates the contribution of our approach: although DECODE can be computationally expensive, it is capable of identifying the number of point processes and simultaneously estimating the classification thresholds with little prior knowledge.

show abstract

An Approach to Find Embedded Clusters Using Density Based Techniques

Cited by 57 publications

References 6 publications

A Vibration Method for Discovering Density Varied Clusters

A Vibration Method for Discovering Density Varied Clusters

An Extended Density based Clustering Algorithm for Large Spatial 3D Data using Polyhedron Approach

DECODE: a new method for discovering clusters of different densities in spatial data

Contact Info

Product

Resources

About