scholarly journals To Enhance Frequent Closed Pattern Tree using Fuzzy Clustering of Personalized Web-Log in Big Data

2016 ◽  
Vol 144 (5) ◽  
pp. 21-24
Author(s):  
Sapana Kumari ◽  
Vikram Garg
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Web Log ◽  
Closed Pattern

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 Detection of Jihadism in Social Networks Using Big Data Techniques Supported by Graphs and Fuzzy Clustering

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Cristina Sánchez-Rebollo ◽  
Cristina Puente ◽  
Rafael Palacios ◽  
Claudia Piriz ◽  
Juan P. Fuentes ◽  
...  
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Fuzzy Clustering ◽  
Extraction Techniques ◽  
Public Database ◽  
New Members ◽  
Terrorist Organizations ◽  
Clustering Techniques ◽  
Level Of Activity ◽  
Data Architecture

Social networks are being used by terrorist organizations to distribute messages with the intention of influencing people and recruiting new members. The research presented in this paper focuses on the analysis of Twitter messages to detect the leaders orchestrating terrorist networks and their followers. A big data architecture is proposed to analyze messages in real time in order to classify users according to different parameters like level of activity, the ability to influence other users, and the contents of their messages. Graphs have been used to analyze how the messages propagate through the network, and this involves a study of the followers based on retweets and general impact on other users. Then, fuzzy clustering techniques were used to classify users in profiles, with the advantage over other classifications techniques of providing a probability for each profile instead of a binary categorization. Algorithms were tested using public database from Kaggle and other Twitter extraction techniques. The resulting profiles detected automatically by the system were manually analyzed, and the parameters that describe each profile correspond to the type of information that any expert may expect. Future applications are not limited to detecting terrorist activism. Human resources departments can apply the power of profile identification to automatically classify candidates, security teams can detect undesirable clients in the financial or insurance sectors, and immigration officers can extract additional insights with these techniques.

scholarly journals An efficient clustering mechanism in big data framework for data preprocessing and management

2020 ◽  
Vol 5 (18) ◽  
pp. 19-25
Author(s):  
Shweta Kumari ◽  
Kailash Patidar ◽  
Rishi Kushwah ◽  
Gaurav Saxena
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Threshold Values ◽  
Final Phase ◽  
Data Framework ◽  
Data Segment ◽  
Efficient Data ◽  
Data Arrangement ◽  
Data Environment

An efficient data handling mechanism has been applied based on epoch-based k-means associated fuzzy clustering (EKFC). In the first phase weights have been assigned to individual data segment presented based on the classification key metrics. It has been assigned automatically. Then weight preprocessing has been done in such manner to prune the unwanted weights. It has been pruned in such way to filter the weights which are not scalable. Then epoch-based k-means associated fuzzy clustering (EKFC) approach has been applied for data arrangement. First different epochs have been considered for the calculation of initial seeds values. These seeds have been considered after considering 100 epochs. After 100 epochs seeds have been determined. These seeds values have been used as the initial centroid for the k-means clustering. After the complete validation similar clusters from the two clustering approaches have been considered. In the next phase operational clustering has been performed. In the final phase threshold ranking has been performed. It has been performed for the final classification based on the above clusters. It will arrange in the order of threshold values. It will be used for the determination of the priority of the task in the big data environment. The results are found to be prominent in terms of classification accuracy.

scholarly journals Fuzzy Latent-Dynamic Conditional Neural Fields for Gesture Recognition in Video

2017 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Intan Nurma Yulita ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Big Data ◽  
Gesture Recognition ◽  
Fuzzy Clustering ◽  
Video Processing ◽  
Random Fields ◽  
Conditional Random Fields ◽  
The Other ◽  
The Internet ◽  
Neural Fields ◽  
Fuzzy C Means Clustering

<p>With the explosion of data on the internet led to the presence of the big data era, so it requires data processing in order to get the useful information. One of the challenges is the gesture recognition the video processing. Therefore, this study proposes Latent-Dynamic Conditional Neural Fields and compares with the other family members of Conditional Random Fields. To improve the accuracy, these methods are combined by using Fuzzy Clustering. From the result, it can be concluded that the performance of Latent-Dynamic Conditional Neural Fields are  lower than Conditional Neural Fields but higher than the Conditional Random Fields and Latent-Dynamic Conditional Random Fields. Also, the combination of Latent-Dynamic Conditional Neural Fields and Fuzzy C-Means Clustering has the highest. This evaluation is tested in a temporal dataset of gesture phase segmentation.</p>

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