Parallel Fuzzy Cognitive Map Using Evolutionary Feature Reduction for Big Data Classification Problem

2018 ◽  
pp. 226-239 ◽  
Author(s):  
M. V. Judy ◽  
Gayathri Soman
Keyword(s):  
Big Data ◽  
Data Classification ◽  
Classification Problem ◽  
Cognitive Map ◽  
Feature Reduction ◽  
Fuzzy Cognitive Map ◽  
Big Data Classification

scholarly journals FCNB: Fuzzy Correlative Naive Bayes Classifier with MapReduce Framework for Big Data Classification

2018 ◽  
Vol 29 (1) ◽  
pp. 994-1006
Author(s):  
Chitrakant Banchhor ◽  
N. Srinivasu
Keyword(s):  
Big Data ◽  
Naive Bayes ◽  
Fuzzy Theory ◽  
Data Classification ◽  
Classification Problem ◽  
Extraction Process ◽  
Naïve Bayes ◽  
Mapreduce Framework ◽  
Skin Segmentation ◽  
Big Data Classification

Abstract The term “big data” means a large amount of data, and big data management refers to the efficient handling, organization, or use of large volumes of structured and unstructured data belonging to an organization. Due to the gradual availability of plenty of raw data, the knowledge extraction process from big data is a very difficult task for most of the classical data mining and machine learning tools. In a previous paper, the correlative naive Bayes (CNB) classifier was developed for big data classification. This work incorporates the fuzzy theory along with the CNB classifier to develop the fuzzy CNB (FCNB) classifier. The proposed FCNB classifier solves the big data classification problem by using the MapReduce framework and thus achieves improved classification results. Initially, the database is converted to the probabilistic index table, in which data and attributes are presented in rows and columns, respectively. Then, the membership degree of the unique symbols present in each attribute of data is found. Finally, the proposed FCNB classifier finds the class of data based on training information. The simulation of the proposed FCNB classifier uses the localization and skin segmentation datasets for the purpose of experimentation. The results of the proposed FCNB classifier are analyzed based on the metrics, such as sensitivity, specificity, and accuracy, and compared with the various existing works.

Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

2021 ◽  
pp. 1-12
Author(s):  
Li Qian
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Classification Accuracy ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Data Classification ◽  
Fisher Discriminant Analysis ◽  
Fuzzy Clustering Algorithm ◽  
Local Fisher Discriminant Analysis ◽  
Big Data Classification

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

scholarly journals Research on the Construction of Big Data Classification System Based on Distributed Data Flow

2021 ◽  
Vol 2136 (1) ◽  
pp. 012057
Author(s):  
Han Zhou
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Data Classification ◽  
Classification Model ◽  
Distributed Data ◽  
Analysis Model ◽  
Database Construction ◽  
Big Data Classification ◽  
Operation Status ◽  
Model Algorithm

Abstract In the context of the comprehensive popularization of network technical services and database construction system, more and more data are used by enterprises or individuals. It is difficult for the existing technology to meet the technical analysis requirements of the development of the era of big data. Therefore, in the development of practice, we should continue to explore new technologies and methods to reasonably use big data. Therefore, on the basis of understanding the current big data technology and its system operation status, this paper designs relevant algorithms according to the big data classification model, and verifies the effectiveness of the analysis model algorithm based on practice.

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