DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques

Smiti, Abir; Elouedi, Zied

doi:10.1109/ines.2012.6249802

Cited by 74 publications

(41 citation statements)

References 9 publications

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“…An unreliable method may result in an incorrect noise ratio that directly affects clustering performance. Smiti et al [7] proposed an efficient clustering technique that combined the DBSCAN and Gaussian-Means (GMeans) algorithms. They used GMeans to partition data into K clusters without a predefined parameter.…”

Section: Related Workmentioning

confidence: 99%

Extraction and Identification of Bar Graph Components by Automatic Epsilon Estimation

Kanjanawattana¹,

Kimura²

2017

IJCTE

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Section: Related Workmentioning

confidence: 99%

Extraction and Identification of Bar Graph Components by Automatic Epsilon Estimation

Kanjanawattana¹,

Kimura²

2017

IJCTE

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“…DBSCAN [3] is a density based algorithm which discovers clusters with arbitrary shape. However, it requires the specification of two input parameters which are hard to guess [18]. The input parameters are the radius of the cluster (Eps) and minimum required points inside the cluster (MinPts).…”

Section: Dbscan: a Density-based Clusteringmentioning

confidence: 99%

“…This method also requires input parameters for grid partitioning. Smiti and Elouedi [18] combine Gaussian-Means (GM) and DBSCAN algorithm to determine the input parameters in DBSCAN. However, GM provides circular cluster shape not density-based clusters, and it is not strong against noise (outlier).…”

Section: Introductionmentioning

confidence: 99%

Choosing DBSCAN Parameters Automatically using Differential Evolution

Karami¹,

Johansson²

2014

IJCA

105

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Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DB-SCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.

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“…Thus, we decide to apply the Clustering method SOFTDBSCAN [7], [11] as a first step in our new RCFM. In fact, applying SOFT-DBSCAN in the database aims at eliminating its noisy and redundant instances.…”

Section: A Maintaining the Databasementioning

confidence: 99%

Robust speech recognition using consensus function based on multi-layer networks

Amami

Manita²,

Smiti

2014

2014 9th Iberian Conference on Information Systems and Technologies (CISTI)

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-The clustering ensembles mingle numerous partitions of a specified data into a single clustering solution. Clustering ensemble has emerged as a potent approach for ameliorating both the forcefulness and the stability of unsupervised classification results. One of the major problems in clustering ensembles is to find the best consensus function. Finding final partition from different clustering results requires skillfulness and robustness of the classification algorithm. In addition, the major problem with the consensus function is its sensitivity to the used data sets quality. This limitation is due to the existence of noisy, silence or redundant data. This paper proposes a novel consensus function of cluster ensembles based on Multilayer networks technique and a maintenance database method. This maintenance database approach is used in order to handle any given noisy speech and, thus, to guarantee the quality of databases. This can generates good results and efficient data partitions. To show its effectiveness, we support our strategy with empirical evaluation using distorted speech from Aurora speech databases.

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DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques

Cited by 74 publications

References 9 publications

Extraction and Identification of Bar Graph Components by Automatic Epsilon Estimation

Extraction and Identification of Bar Graph Components by Automatic Epsilon Estimation

Choosing DBSCAN Parameters Automatically using Differential Evolution

Robust speech recognition using consensus function based on multi-layer networks

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