ADVANTAGE AND DISADVANTAGE OF VARIOUS METHODS OF DATA MINING CLASSIFICATION (Assignmen 5)

ADVANTAGE AND DISADVANTAGE OF VARIOUS METHODS OF DATA MINING CLASSIFICATION
Method	Advantage	Disadvantage
Naïve Bayes	Handles quantitative and discrete data	Not applicable if the conditional probability is zero, if zero then the predicted probability will be zero as well
	Sturdy for isolated noise points, let's say the points are averaged when estimating conditional data opportunities.	Assuming variableas independent
	It requires only a small amount of training data to estimate the parameters (average and variance of variables) required for classification.
	Handle the lost value by ignoring the agency during the estimated opportunity calculation
	Fast and space efficiency
	Sturdy against irrelevant attributes
Decision Tree	A complex and very global area of decision-making, can be changed to be more simple and specific.	Overlap occurs especially when the classes and criteria used are very large. It can also lead to increased decision-making time and the amount of memory required.
	Eliminate unnecessary calculations.	Accumulate the number of errors from each level in a large decision tree.
	Flexible to select features from different internal nodes	Difficulty in designing the optimal decision tree. The result of the decision quality obtained from the decision tree method depends on how the tree is designed.
	The decision tree method can avoid the emergence of this problem by using fewer criteria on each internal node without significantly reducing the quality of the resulting decision.
K-NN	KNN has some advantages that he is tough to training data that noisy and effective when the data training him great.	It is necessary to determine the most optimal k value which states the number of nearest neighbors
	More effective in large training data	The computational cost is quite high because distance calculations must be performed on each query instance together with all instances of the training sample
	Can produce more accurate data

Preference:
Florin Gorunescu, Data Mining: Concepts, Models and Techniques, Springer, 2011.
Jiawei Han and Micheline Kamber, Data Mining:Concepts and TechniquesSecond Edition, Elsevier, 2006
Ian H. Witten, Frank Eibe, Mark A. Hall, Data mining: Practical Machine Learning Tools and Techniques3rd Edition, Elsevier, 2011.

North, M. (2012). Data Mining for the Masses. USA:Creative Commons Attribution.