Fast Efficient Clustering Algorithm for Balanced Data

Sewisy, Adel A.; El-Aziz, Rasha M. Abd; Marghny, M. H.; Taloba, Ahmed I.

doi:10.2139/ssrn.2545138

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…The experiments for data mining analysis have been done using the clustering algorithm. This algorithm has been selected because it needs a large amount of computational time for handling large data sets (Sewisy et al, 2014). As a referential tool for the conducted experiments we have used WEKA (Hall et al, 2009), a tool for automatic learning and data mining algorithms, similar researches based on WEKA have been done (Srivastava, 2014;Rupali et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Model of Cloud-Based Services for Data Mining Analysis

Karadimce¹

2015

CIS

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New cloud-based services are being developed constantly in order to meet the need for faster, reliable and scalable methods for knowledge discovery. The major benefit of the cloud-based services is the efficient execution of heavy computation algorithms in the cloud simply by using Big Data storage and processing platforms. Therefore, we have proposed a model that provides data mining techniques as cloud-based services that are available to users on their demand. The widely known data mining algorithms have been implemented as Map/Reduce jobs that are been executed as services in cloud architecture. The user simply chooses or uploads the dataset to the cloud, makes appropriate settings for the data mining algorithm, executes the job request to be processed and receives the results. The major benefit of this model of cloud-based services is the efficient execution of heavy computation data mining algorithm in the cloud simply by using the Ankus -Open Source Big Data Mining Tool and StarfishHadoop Log Analyzer. The expected outcome of this research is to offer the integration of the cloud-based services for data mining analysis in order to provide researchers with reliable collaborative data mining analysis model.

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Section: Resultsmentioning

confidence: 99%

Model of Cloud-Based Services for Data Mining Analysis

Karadimce¹

2015

CIS

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“…The K-means algorithm is one of the most important unsupervised clustering algorithms [17] that produces high quality results in less computation time [1]. The K-means machine learning algorithm [18] is used to group a known, assumed, or indicated in advance dataset.…”

Section: A K-means Algorithmmentioning

confidence: 99%

“…The objective of clustering is to classify a set of elements into groups that are very similar among them, but different with elements from other groups. Author in [1] consider the k-Means grouping algorithm, to be one of the most efficient grouping algorithms for large-scale data sets.…”

Section: Introductionmentioning

confidence: 99%

Classification Model of Municipal Management in Local Governments of Peru based on K-means Clustering Algorithms

Morales¹,

Vargas²,

Coyla³

et al. 2020

IJACSA

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The K-means algorithm groups datasets into different groups, defines a fixed number of clusters, iteratively assigning data to the clusters formed by adjusting the centers in each cluster. K-means algorithm uses an unsupervised learning method to discover patterns in an input data set. The purpose of the research is to propose a municipal management classification model in the municipalities of Peru using a K-means clustering algorithm based in 58 variables obtained from the areas of human resources, heavy machinery and operating vehicles, information and communication technologies, municipal planning, municipal finances, local economic development, social services, solid waste management, cultural, recreational and sports facilities, public security, disaster risk management, environmental protection and conservation of all the municipalities of the 24 departments of Peru and the constitutional province of Callao. The results of the application of the K-means algorithm show that 32% of the municipalities made up of the municipal governments of Amazonas,

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“…The similarity or dissimilarity between objects is based on a specific distance function. Clustering algorithms can be classified into partitional [1][2][3], hierarchical [4,5], density-based [6][7][8][9][10], and grid-based [11] methods. Density-based methods consider clusters as high-density regions that are separated from each other by low-density ones.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities

Fahim¹

2023

Computers, Materials &Amp; Continua

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Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: epsilon (fixed neighborhood radius) and MinPts (the lowest number of objects in epsilon). However, it can't handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one. Only user input in the proposed method is the MinPts. Epsilon on the other hand, is computed automatically based on statistical information of the dataset. The proposed method finds the core distance for each object in the dataset, takes the average of these distances as the first value of epsilon, and finds the clusters satisfying this density level. The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects. This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset's size. The proposed method requires MinPts only as an input parameter because epsilon is computed from data. Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results. Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them. The accuracy of the method ranges from 92% to 100% for the experimented datasets.

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Fast Efficient Clustering Algorithm for Balanced Data

Cited by 9 publications

References 6 publications

Model of Cloud-Based Services for Data Mining Analysis

Model of Cloud-Based Services for Data Mining Analysis

Classification Model of Municipal Management in Local Governments of Peru based on K-means Clustering Algorithms

Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities

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