Survey on clustering methods: Towards fuzzy clustering for big data

Ayed, Abdelkarim Ben; Halima, Mohamed Ben; Alimi, Adel M.

doi:10.1109/socpar.2014.7008028

Cited by 44 publications

(20 citation statements)

References 18 publications

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“…To be able to run the MST-based cluster number estimation algorithms on a computer having a small number of available memory, the number of distinct edges cannot be greater than the quotient of divided by . Let denote , substitute to (5) and (6), achieve (8).…”

Section: B a Cell-based "Data-to-grid" Methodsmentioning

confidence: 99%

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A cell-MST-based method for big dataset clustering on limited memory computers

Hieu

Meesad

2015

2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE)

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This paper presents a new clustering algorithm, called Cell-MST-Based Method that is a combination of a Cellbased method and Minimum Spanning Tree based (MST-based) methods. The algorithm is dedicated for Big Datasets on a limited memory computer, especially for thin big datasets which have a small number of attributes but a very large number of instances. Firstly, a Cell-based method converts a big dataset to a small grid of cells in such a way that the required memory to store an edgeweighted graph created from the grid which is less than the available memory of a computer. Then MST-based methods obtain an optimal threshold, estimate the number of clusters and determine the initial centroids. The proposed Cell-MST-based methods can reduce more than 99% of the required memory of the previous similarity-based and MST-based cluster number estimation methods. Moreover, this new Cell-MST-based method also outperforms the quantization error modeling method in terms of executing time and estimated accurate level.

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Section: B a Cell-based "Data-to-grid" Methodsmentioning

confidence: 99%

“…Hard partitioning clustering is to divide a dataset = ( 1 , 2 , … , ) into disjoined partitions in such a way that the sum of within-cluster distances will be minimized and the sum of inter-cluster distances will be maximized [5].…”

Section: A Partioning Clusteringmentioning

confidence: 99%

A cell-MST-based method for big dataset clustering on limited memory computers

Hieu

Meesad

2015

2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE)

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“…A few outstanding reasons [2,3] are noted below. According to the geographic limits and endeavors, the culture of organizations, etc.…”

Section: Why Analyze Crimes?mentioning

confidence: 99%

Using Mapreduce Techniques to Predict and Examine Crime Pattern

Kumar¹,

Vishnudas²,

Kayalvizhi³

2018

IJET

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The evolution of computer structures and networks has created an alternative set for crook acts, extensively known as the crime. Crime incidents occurrences of specific criminal offenses lead to a heavy risk to the world economy, protection, and well-being of society. This paper provides complete information of crime incidents and their corresponding offenses combining a sequence of strategies in line with the appropriate literature. Initially, this paper evaluates and identifies the alternatives to crime incidents, their individual components and proposes a combinatorial incident-description schema. The schema offers the chance to systematically blend various elements or crime traits. Moreover, a complete listing of crime-associated offenses is provided in this paper. So, to increase the performance of crime detection, it is essential to choose the data mining strategies appropriately. Hadoop enables to solve the crime as a radical expertise of the repetition and underlying criminal activities. Using Hadoop, we can locate the specific city and analyze the crime patterns, based on that give preventive measures to people.

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“…The clustering techniques have a variety of concepts. The use of clustering techniques depends on applied fields [9]. For the simple and effective clustering techniques, there are several algorithms such as K-means, Hierarchical Clustering and Expectation-Minimization that are discussed below.…”

Section: B Clusteringmentioning

confidence: 99%

A survey of data mining techniques for analyzing crime patterns

Thongsatapornwatana¹

2016

2016 Second Asian Conference on Defence Technology (ACDT)

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In recent years the data mining is data analyzing techniques that used to analyze crime data previously stored from various sources to find patterns and trends in crimes. In additional, it can be applied to increase efficiency in solving the crimes faster and also can be applied to automatically notify the crimes. However, there are many data mining techniques. In order to increase efficiency of crime detection, it is necessary to select the data mining techniques suitably. This paper reviews the literatures on various data mining applications, especially applications that applied to solve the crimes. Survey also throws light on research gaps and challenges of crime data mining. In additional to that, this paper provides insight about the data mining for finding the patterns and trends in crime to be used appropriately and to be a help for beginners in the research of crime data mining.

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Survey on clustering methods: Towards fuzzy clustering for big data

Cited by 44 publications

References 18 publications

A cell-MST-based method for big dataset clustering on limited memory computers

A cell-MST-based method for big dataset clustering on limited memory computers

Using Mapreduce Techniques to Predict and Examine Crime Pattern

A survey of data mining techniques for analyzing crime patterns

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