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

1. 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

2. 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.

3. 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.

4. 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.

5. RELATED HYBRID ALGORITHMS FOR
BIG DATA MINING
 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.

6. RELATED HYBRID ALGORITHMS FOR
BIG DATA MINING
 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.

7. RELATED HYBRID ALGORITHMS FOR
BIG DATA MINING
 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

8. RELATED HYBRID ALGORITHMS FOR
BIG DATA MINING
 Apriori-MapReduce Algorithm
 Apriori algorithm is redesigned into a map
reduce platform; therefore increase the
efficiency upto 15 %.

9. RELATED HYBRID ALGORITHMS FOR
BIG DATA MINING
 Hybrid GA-SVM model

10. 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

11. 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.

12. REFERENCES
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 Thamilselvan, P., & Sathiaseelan, J. G. R. (2015, March). Image classification using hybrid data mining algorithms-a review. In Innovations in Information,
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13. REFERENCES
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