A Novel Approach for Data Mining Clustering Technique Using NeuralGas Algorithm

2014 ◽  
Author(s):  
Prashant Richhariya ◽  
Anurag Shrivastava ◽  
Mohnish Patel
Keyword(s):  
Data Mining ◽  
Clustering Technique ◽  
Novel Approach

Membership Calculation Based on Dimension Hierarchical Division

2013 ◽  
Vol 475-476 ◽  
pp. 312-317
Author(s):  
Ping Zhou ◽  
Jin Lei Wang ◽  
Xian Kai Chen ◽  
Guan Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Bottom Up ◽  
Generalization Ability ◽  
Clustering Technique ◽  
Novel Approach ◽  
Training Samples ◽  
Noise Sample

Since dataset usually contain noises, it is very helpful to find out and remove the noise in a preprocessing step. Fuzzy membership can measure a samples weight. The weight should be smaller for noise sample but bigger for important sample. Therefore, appropriate sample memberships are vital. The article proposed a novel approach, Membership Calculate based on Hierarchical Division (MCHD), to calculate the membership of training samples. MCHD uses the conception of dimension similarity, which develop a bottom-up clustering technique to calculate the sample membership iteratively. The experiment indicates that MCHD can effectively detect noise and removes them from the dataset. Fuzzy support vector machine based on MCHD outperforms most of approaches published recently and hold the better generalization ability to handle the noise.

A Novel Approach Using Steganography and Cryptography in Business Intelligence

2021 ◽  
pp. 192-217
Author(s):  
Sabyasachi Pramanik ◽  
Ramkrishna Ghosh ◽  
Mangesh M. Ghonge ◽  
Vipul Narayan ◽  
Mudita Sinha ◽  
...  
Keyword(s):  
Data Mining ◽  
Image Encryption ◽  
Encryption Scheme ◽  
Secret Key ◽  
Cryptographic Algorithm ◽  
Novel Approach ◽  
Optimal Accuracy ◽  
Direct Data ◽  
Lsb Technique ◽  
Encryption Method

In the information technology community, communication is a vital issue. And image transfer creates a major role in the communication of data through various insecure channels. Security concerns may forestall the direct sharing of information and how these different gatherings cooperatively direct data mining without penetrating information security presents a challenge. Cryptography includes changing over a message text into an unintelligible figure and steganography inserts message into a spread media and shroud its reality. Both these plans are successfully actualized in images. To facilitate a safer transfer of image, many cryptosystems have been proposed for the image encryption scheme. This chapter proposes an innovative image encryption method that is quicker than the current researches. The secret key is encrypted using an asymmetric cryptographic algorithm and it is embedded in the ciphered image using the LSB technique. Statistical analysis of the proposed approach shows that the researcher's approach is faster and has optimal accuracy.

Graph-Based Modelling of Concurrent Sequential Patterns

2010 ◽  
Vol 6 (2) ◽  
pp. 41-58 ◽  
Author(s):  
Jing Lu ◽  
Weiru Chen ◽  
Malcolm Keech
Keyword(s):  
Data Mining ◽  
Graph Representation ◽  
Sequential Patterns ◽  
Mining Method ◽  
Post Processing ◽  
Synthetic Sample ◽  
Novel Approach ◽  
Sample Data ◽  
Modelling Methods ◽  
Graph Based Model

Structural relation patterns have been introduced recently to extend the search for complex patterns often hidden behind large sequences of data. This has motivated a novel approach to sequential patterns post-processing and a corresponding data mining method was proposed for Concurrent Sequential Patterns (ConSP). This article refines the approach in the context of ConSP modelling, where a companion graph-based model is devised as an extension of previous work. Two new modelling methods are presented here together with a construction algorithm, to complete the transformation of concurrent sequential patterns to a ConSP-Graph representation. Customer orders data is used to demonstrate the effectiveness of ConSP mining while synthetic sample data highlights the strength of the modelling technique, illuminating the theories developed.

scholarly journals Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation

2019 ◽  
Vol 29 (1) ◽  
pp. 1441-1452 ◽  
Author(s):  
G.K. Shailaja ◽  
C.V. Guru Rao
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Original Data ◽  
Cuckoo Search Algorithm ◽  
Research Issues ◽  
Novel Approach ◽  
Two Phases

Abstract Privacy-preserving data mining (PPDM) is a novel approach that has emerged in the market to take care of privacy issues. The intention of PPDM is to build up data-mining techniques without raising the risk of mishandling of the data exploited to generate those schemes. The conventional works include numerous techniques, most of which employ some form of transformation on the original data to guarantee privacy preservation. However, these schemes are quite multifaceted and memory intensive, thus leading to restricted exploitation of these methods. Hence, this paper intends to develop a novel PPDM technique, which involves two phases, namely, data sanitization and data restoration. Initially, the association rules are extracted from the database before proceeding with the two phases. In both the sanitization and restoration processes, key extraction plays a major role, which is selected optimally using Opposition Intensity-based Cuckoo Search Algorithm, which is the modified format of Cuckoo Search Algorithm. Here, four research issues, such as hiding failure rate, information preservation rate, and false rule generation, and degree of modification are minimized using the adopted sanitization and restoration processes.

Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques

2018 ◽  
Vol 5 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Sharmila Subudhi ◽  
Suvasini Panigrahi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Automobile Insurance ◽  
Test Set ◽  
Data Mining Techniques ◽  
Weighted Extreme Learning Machine ◽  
Original Dataset ◽  
Novel Approach ◽  
Learning Machine

This article presents a novel approach for fraud detection in automobile insurance claims by applying various data mining techniques. Initially, the most relevant attributes are chosen from the original dataset by using an evolutionary algorithm based feature selection method. A test set is then extracted from the selected attribute set and the remaining dataset is subjected to the Possibilistic Fuzzy C-Means (PFCM) clustering technique for the undersampling approach. The 10-fold cross validation method is then used on the balanced dataset for training and validating a group of Weighted Extreme Learning Machine (WELM) classifiers generated from various combinations of WELM parameters. Finally, the test set is applied on the best performing model for classification purpose. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world automobile insurance defraud dataset. Besides, a comparative analysis with another approach justifies the superiority of the proposed system.

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