ppt for paper id 696 a review of hybrid data mining algorithm for big data mining

A Review of Hybrid Data Mining Algorithm for Big Data Mining

Presented By

PRASANTA KUMAR PAUL

RESEARCH SCHOLAR

AIIT

AMITY UNIVERSITY RAJASTHAN

First International Conference on Smart Technologies in Computer and Communication (SmartTech-2017)

Under the guidance of DR. SONALI VYAS

ASSISTANT PROFESSOR

AIIT

AMITY UNIVERSITY RAJASTHAN

What is …… ?

• Hybrid Data Mining

‣ Hybrid data mining algorithm can be presented as a combination of different classifiers. The classification ability of data mining algorithm are different, this why combining them may increase the performance of the system in term of accuracy. But they must be well chosen. There are other approach which are more general Boosting and Bagging. They are very interesting and can be efficient. An example of application in image processing is the face detection in real time using Adaboost.

LITERATURE SURVEY

P Thamilselvan Image classification using hybrid data mining algorithm.

Deshmukh, A. P., & Pamu, K. S. (2012).Introduction to Hadoop distributed file system .

Feilong Cao, proposed a new algorithm, combination of Extreme K-Means (EKM) and Effective Extreme Learning Machine (EELM)

Alireza Taravat et al, introduced a new hybrid algorithm for automatic cloud detection in a complete-sky image.

M.R. et al. [10] in this study they presented a hybrid algorithm using Support Vector Machine (SVM) and K-nearest neighbor (KNN) algorithm.

RELATED HYBRID ALGORITHMS FOR BIG DATA MINING

Hybrid evolutionary clustering with empty clustering solution (H (EC) 2 S)

RC Part (Representative Construction):

EFC Part (Enhanced Fireworks algorithm for clustering):

CSC Part (Cuckoo search for clustering):

Hybrid evolution clustering with empty clustering solution (H (EC) 2 S) indicates better precision when contrasted with other hybrid approaches.


Hybrid Clustering Algorithm (HBCA) using BIRCH and K-Means

Hybrid Clustering Algorithm (HBCA) using BRICH and K-Means, This proposed method gives better performance then K-Means and K-medoid. By using WEKA datamining tool.


GA/DT Hybrid data mining algorithm

GA/DT Hybrid data mining algorithm, This proposed method gives 20 % more effective then the decision tree and genetic programming individually.


VAMR Algorithm- Vertical-Apriori MapReduce algorithm

Initial scan

Producing frequent 1-item set and its TID set

Producing frequent (K+1) item set

More Applicants

END


Apriori-MapReduce Algorithm

Apriori algorithm is redesigned into a map reduce platform; therefore increase the efficiency upto 15 %.


Hybrid GA-SVM model

COMPARISON OF DIFFERENT HYBRID DATA MINING ALGORITHMS BASED ON IMAGE CLASSIFICATION

Table 1

Narration of Hybrid Algorithm (Base on Image Classification)

S.No Proposed hybrid Approach Purpose of development Draw backs

1 Genetic Algorithm and Support Vector Machine To reduce the dimensionality and optimize the classification process

Display the high error rate.

2 Decision Tree and Naive Bayes To improve the classification accuracy of multi class problem

Given less compact Solution.

3 Extreme K-Means and Effective Extreme learning Machine

To improve the classification accuracy

Process rate is very slow for Training.

4 Naïve Bayes and Support Vector machine To improve the performance of specificity and sensitivity

Several key parameters needed to achieve the best classification result.

5 Support Vector Machine and Classification regression tree

To identify the age band of 2D image face.

The regression provide highly confusion

CONCLUSION AND FUTURE WORK

The proposed Methodology provides a comprehensive knowledge about how to deal with large datasets. The methodology is easy but requires good knowledge of data mining.

From this review the hybrid method Hybrid evolution clustering with empty clustering solution (H (EC) 2 S) indicates better precision when contrasted with other hybrid approaches.

In future, we means to consolidate at least two data mining methods. By applying the proposed hybrid technique, it is planned to discover better classification precision and besides, reduce the computational time complexity then another hybrid method.

REFERENCES

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