Hybrid data mining algorithm in cloud computing using MapReduce framework

Sahay, Siddharth; Khetarpal, Suruchi; Pradhan, Tribikram

doi:10.1109/icaccct.2016.7831691

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…Eğer bir öğe kümesi yaygınsa ve bu öğe kümesinin yaygın bir üst kümesi yoksa, maksimal olarak isimlendirilir. Bulut ortamında da kapalı örüntü [52] ve maksimal örüntü [53] kavramları uygulayan çalışmalar bulunmaktadır.…”

Section: Bulut Bilişimde Kümeleme Yöntemiunclassified

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Application of data mining techniques in cloud computing: A literature review

Yıldırım¹,

Birant²

2018

Pamukkale J Eng Sci

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Son yıllarda, bilgi ve iletişim teknolojilerindeki yenilikler ve gelişmeler, analiz edilmesi gereken veri miktarını önemli derecede artırmıştır. Büyük ölçekli verilerin saklanması, yüksek hacimli verilerden veri madenciliği teknikleri ile yararlı bilgilerin çıkartılması ve geleceğin tahminlenmesi maliyetli ve zorlu işlemlerdir. Bu zorlukların üstesinden gelebilmek için, bilgi keşfi süreci; bulut bilişim, paralel ve dağıtık hesaplama kullanılarak etkin bir şekilde gerçekleştirilebilmektedir. Bu makale, bulut bilişimin ölçeklenebilirliği sayesinde veri madenciliği algoritmalarının performanslarının arttırılabileceğini, ayrıca her yerden ulaşılabilirlik, düşük maliyet ve kolay yönetilebilirlik avantajlarının sağlanabileceğini göstermektedir. Makalede, bulut platformunda gerçekleştirilen veri madenciliği uygulamaları; kullanılan metotlar, veriler ve elde edilen sonuçlar çerçevesinde sunulmaktadır. Literatürde, bu konuda önerilen çözüm yaklaşımları; sınıflandırma, kümeleme ve birliktelik kuralları analizi olmak üzere üç ana kategori altında ele alınmaktadır. In recent years, new innovations and developments in information and communication technologies have hugely increased the quantity of data required to analyze. Storing large-scale datasets, extracting useful knowledge from the huge volumes of data by applying data mining techniques and predicting the future are costly and difficult processes. To overcome these challenges, the knowledge discovery process is performed efficiently by using cloud, parallel and distributed computing. This article shows that the performance algorithms in data mining can be increased by the scalability of cloud computing with the advantages in terms of accessibility from anywhere, low cost and maintainability. In this article, data mining applications that have been implemented on cloud platforms are presented, including methods, data and obtained results. Solution approaches that have been proposed related to this topic in the literature are handled in three main categories: classification, clustering and association rule mining.

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Section: Bulut Bilişimde Kümeleme Yöntemiunclassified

“…Diğer veri madenciliği yöntemlerinin bulut üzerinde uygulanmasında olduğu gibi, birliktelik kuralları analizinde de Hadoop ve MapReduce sistemleri oldukça sık kullanılmaktadır [42], [46]- [48], [51]- [53].…”

Section: Bulut Bilişimde Kümeleme Yöntemiunclassified

Application of data mining techniques in cloud computing: A literature review

Yıldırım¹,

Birant²

2018

Pamukkale J Eng Sci

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“…Hybrid approaches combine efficient parts of certain algorithms into new wholes, enhancing the accuracy and the efficiency of these algorithms, or producing novel algorithms. In literature, there are many successful hybrid data-mining approaches: Kumar et al implemented a highly accurate optimization algorithm from the combination of a genetic algorithm with fuzzy logic and ANN [42]; Singhal and Ashraf implemented a high-performance classification algorithm from the combination of a decision tree and a genetic algorithm [43]; Hassan and Verma collected successful highaccuracy hybrid data-mining applications for the medical domain in their study [44]; amilselvan and Sathiaseelan reviewed hybrid data-mining algorithms for image classification [45]; Athiyaman et al implemented a high-accuracy approach combination of association rule mining algorithms and clustering algorithms for meteorological datasets [46]; Sahay et al proposed a high-performance hybrid data-mining approach combining apriori and K-means algorithms for cloud computing [47]; Yu et al obtained a novel solution selection strategy using hybrid clustering algorithms [48]; Sitek and Wikarek implemented a hybrid framework for solving optimization problems and constraint satisfaction by using constraint logic programming, constraint programming, and mathematical programming [49]; Abdel-Maksoud et al proposed a hybrid clustering technique combining K-means and fuzzy C-means algorithms to detect brain tumours with high accuracy and performance [50]; Zhu et al implemented a novel high-performance hybrid approach containing hierarchical clustering algorithms for the structure of wireless networks [51]; Rahman and Islam combined K-means and a genetic algorithm to obtain a novel high-performance genetic algorithm [52]; and Jagtap proposed a high-accuracy technique to diagnose heart disease by combining Naïve Bayes, Multilayer Perceptron, C4.5 as a decision tree algorithm, and linear regression [53]. What we can infer from a detailed examination of these studies is that K-means and genetic algorithms, and their variants, can be adapted to other algorithms to implement a hybrid approach successfully.…”

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Novel Two-Dimensional Visualization Approaches for Multivariate Centroids of Clustering Algorithms

Doğan

Dalkılıç

Birant

et al. 2018

Scientific Programming

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The dimensionality reduction and visualization problems associated with multivariate centroids obtained by clustering algorithms are addressed in this paper. Two approaches are used in the literature for the solution of such problems, specifically, the self-organizing map (SOM) approach and mapping selected two features manually (MS2Fs). In addition, principle component analysis (PCA) was evaluated as a component for solving this problem on supervised datasets. Each of these traditional approaches has drawbacks: if SOM runs with a small map size, all centroids are located contiguously rather than at their original distances according to the high-dimensional structure; MS2Fs is not an efficient method because it does not take features outside of the method into account, and lastly, PCA is a supervised method and loses the most valuable feature. In this study, five novel hybrid approaches were proposed to eliminate these drawbacks by using the quantum genetic algorithm (QGA) method and four feature selection methods, Pearson’s correlation, gain ratio, information gain, and relief methods. Experimental results demonstrate that, for 14 datasets of different sizes, the prediction accuracy of the proposed weighted clustering approaches is higher than the traditional K-means++ clustering approach. Furthermore, the proposed approach combined with K-means++ and QGA shows the most efficient placements of the centroids on a two-dimensional map for all the test datasets.

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Hybrid data mining algorithm in cloud computing using MapReduce framework

Cited by 2 publications

References 3 publications

Application of data mining techniques in cloud computing: A literature review

Application of data mining techniques in cloud computing: A literature review

Novel Two-Dimensional Visualization Approaches for Multivariate Centroids of Clustering Algorithms

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