scholarly journals NFDDC: Novel Neuro-Fuzzy Logic based Methodology for Distributed Data Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 5370-5375
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Big Data ◽  
Data Classification ◽  
Classification Model ◽  
Distributed Data ◽  
Data Mining Technique ◽  
Data Set ◽  
Internet Applications ◽  
Neuro Fuzzy

With the growing culture of Internet applications and their usage lead to challenging task for storing a massive volume of high-velocity data from different fields. This result an evolution of big data with integrated, i.e. Volume, Velocity, and Variety (3V's). The voluminous data extraction is a very complex task which is not possible form classical data mining techniques. Therefore, a big data mining technique is introducing by modifying traditional data mining scheme using a novel of Neuro-Fuzzy Logic based approach, i.e. named as NFDDC. The proposed distributed data classification model performs into three stages first- reduce the data set dimension, second- data clustering, and third-data classification using the neuro-fuzzy method. The performance of the NFDDC system is analysed using two different datasets, i.e. medical data and e-commerce datasets. Additionally, comparative analysis is performed by measuring the accuracy of existing CCSA algorithm with proposed NFDDC algorithm and will get 90% accuracy in data classification

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.

scholarly journals Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ge Zhou
Keyword(s):  
Data Mining ◽  
Mobile Internet ◽  
Distributed Data ◽  
Cloud Platform ◽  
Data Set ◽  
Internet Applications ◽  
Internet Service ◽  
Clustering Quality ◽  
Efficient Data ◽  
Application Aware

Because the static cloud platform cannot satisfy the diversity of mobile Internet service and inefficient data mining problems, we presented a reliable and efficient data mining cloud platform construction scheme based on the mobile Internet service opportunistic driving and application perception. In this scheme, first of all data selection mechanism was established based on mobile Internet service opportunistic drive. Secondly, through the cloud platform different cloud and channel aware, nonlinear mapping from the service to a data set of proposed perceptual model is applied. Finally, on the basis of the driving characteristics and extraction of perceptual features, the cloud platform would be constructed through the service opportunities of mobile Internet applications, which could provide robust and efficient data mining services. The experimental results show that the proposed mechanism, compared to the cloud platform based on distributed data mining, has obvious advantages in system running time, memory usage, and data clustering required time, as well as average clustering quality.

Privacy Preserving Data Mining on Unstructured Data

2017 ◽  
pp. 167-190
Author(s):  
Trupti Vishwambhar Kenekar ◽  
Ajay R. Dani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Structure Data ◽  
Data Privacy ◽  
Differential Privacy ◽  
Unstructured Data ◽  
Map Reduce ◽  
Individual Data ◽  
Data Set ◽  
Privacy Preserving Data Mining

As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less time for privately mining large dataset. The chapter discussed problem of preserving data privacy, different challenges to preserving data privacy in big data environment, Data privacy techniques and their applications to unstructured data. The analyses of experimental results on structured and unstructured data set are also presented.

scholarly journals The Observation Report of Red Blood Cell Morphology in Thailand Teenager by Using Data Mining Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Sarawut Saichanma ◽  
Sucha Chulsomlee ◽  
Nonthaya Thangrua ◽  
Pornsuri Pongsuchart ◽  
Duangmanee Sanmun
Keyword(s):  
Data Mining ◽  
Blood Cell ◽  
Hematological Parameters ◽  
Medical Science ◽  
Peripheral Blood Smear ◽  
Medical Laboratory ◽  
Data Mining Technique ◽  
Data Set ◽  
Mining Technique ◽  
Laboratory Results

It is undeniable that laboratory information is important in healthcare in many ways such as management, planning, and quality improvement. Laboratory diagnosis and laboratory results from each patient are organized from every treatment. These data are useful for retrospective study exploring a relationship between laboratory results and diseases. By doing so, it increases efficiency in diagnosis and quality in laboratory report. Our study will utilize J48 algorithm, a data mining technique to predict abnormality in peripheral blood smear from 1,362 students by using 13 data set of hematological parameters gathered from automated blood cell counter. We found that the decision tree which is created from the algorithm can be used as a practical guideline for RBC morphology prediction by using 4 hematological parameters (MCV, MCH, Hct, and RBC). The average prediction of RBC morphology has true positive, false positive, precision, recall, and accuracy of 0.940, 0.050, 0.945, 0.940, and 0.943, respectively. A newly found paradigm in managing medical laboratory information will be helpful in organizing, researching, and assisting correlation in multiple disciplinary other than medical science which will eventually lead to an improvement in quality of test results and more accurate diagnosis.

Grid-Based Platform for Mining Association Rules

2010 ◽  
Vol 34-35 ◽  
pp. 1961-1965
Author(s):  
You Qu Chang ◽  
Guo Ping Hou ◽  
Huai Yong Deng
Keyword(s):  
Data Mining ◽  
Large Datasets ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Data Set ◽  
Efficiency And Effectiveness ◽  
Geographically Distributed ◽  
Commercial Applications ◽  
Rule Interestingness ◽  
Grid Based

distributed data mining is widely used in industrial and commercial applications to analyze large datasets maintained over geographically distributed sites. This paper discusses the disadvantages of existing distributed data mining systems, and puts forward a distributed data mining platform based grid computing. The experiments done on a data set showed that the proposed approach produces meaningful results and has reasonable efficiency and effectiveness providing a trade-off between runtime and rule interestingness.

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