Fuzzy K-mean clustering in MapReduce on cloud based hadoop

Currently, we are witnessing a growing trend in the study and application of problems in the framework of Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a high volume of information. For this reason, we observe a migration of the standard Data Mining systems towards a new functional paradigm that allows at working with Big Data. By means of the MapReduce model and its different extensions, scalability can be successfully addressed, while maintaining a good fault tolerance during the execution of the algorithms. Among the different approaches used in Data Mining, those models based on fuzzy systems stand out for many applications. Among their advantages, we must stress the use of a representation close to the natural language. Additionally, they use an inference model that allows a good adaptation to different scenarios, especially those with a given degree of uncertainty. Despite the success of this type of systems, their migration to the Big Data environment in the different learning areas is at a preliminary stage yet. In this paper, we will carry out an overview of the main existing proposals on the topic, analyzing the design of these models. Additionally, we will discuss those problems related to the data distribution and parallelization of the current algorithms, and also its relationship with the fuzzy representation of the information. Finally, we will provide our view on the expectations for the future in this framework according to the design of those methods based on fuzzy sets, as well as the open challenges on the topic.

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“…In Ref. 25, authors use Mahout 50 to study the performance of the fuzzy cmeans implementation. Finally, in Ref.…”

Section: What Has Been Done?mentioning

confidence: 99%

A View on Fuzzy Systems for Big Data: Progress and Opportunities

Fernández¹,

Carmona²,

Jesus³

et al. 2016

IJCIS

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“…The object space is quantized into a finite number of cells which create a grid structure [5]. This method is known as the grid based method.…”

Section: Grid Based Methodsmentioning

confidence: 99%

Two-step Technique for Prediction Analysis using K-Means Clustering Algorithm

Saxena¹,

Kumar²,

Tewari³

2017

IJCA

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The technique that is utilized for analyzing the complex data is known as data mining technique. As per the input dataset provided, the predictions are made for the data with the help of prediction analysis method. There are various new techniques proposed for the execution of prediction analysis technique. In this paper, the k-mean algorithm is utilized for categorizing the data. Further, for the classification of this data, the SVM classifier is applied. For improving the performance of prediction analysis in terms of accuracy the back propagation algorithm is used along with the k-mean clustering algorithm. For executing this proposed technique, the MATLAB tool is used. As per the experimental results it is concluded that the accuracy of the clustering algorithm is improved as well as the execution time utilized for prediction analysis is decreased.

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