S. No

DM Algorithm

Technique used

No. of Papers

Implemented

Merits

Limitations

1

Support Vector Machines

Classification

2, 15, 16, 18, 21, 25

Very accurate, less over fitting, robust to noise.

Binary classifier, In multi-class classification, kernel selection and interpretability are some weaknesses of SVM, Computationally expensive, runs slow.

2

Bayesian Networks

Classification

2, 15, 16

Missing data entries can be handled successfully, Over-fitting of data is avoidable

Quality and extent of prior knowledge play an important role, Significant computational cost

3

Decision Tree

Classification

2, 15, 16, 18

Easy to understand, Easy to generate rules

Over fitting, does not handle easily non numeric data, can be quite large pruning is necessary

4

C4.5

Classification

15, 16, 25

Quite fast, Output is human readable.

Small variation in data can lead to different decision tree, does not work very well on small training data set, Over fitting

5

K-Nearest Neighbor

Classification

2, 15, 16, 18, 25

Ease of understanding and implementation, depending on the distance metric, KNN can be quite accurate

Computationally expensive Noisy data can throw off kNN classifications. kNN generally requires greater storage requirements than eager classifiers, Selecting a good distance metric is crucial to kNN’s accuracy

6

K-Means

Clustering

2, 15, 16, 18

Faster and more efficient especially over large datasets

Sensitive to outliers and the initial choice of centroids, It is designed to operate on continuous data—extra tricks are needed to work on discrete data