2 Analyze and discuss the various data mining techniques Spe

2. Analyze and discuss the various data mining techniques. Specify from your perspective, which technique do you prefer among these? Why?

Solution

Different data mining techniques are:

A)Classification:

Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data.For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks.The simplest type of classification problem is binary classification. In binary classification, the target attribute has only two possible values: for example, high credit rating or low credit rating. Multiclass targets have more than two values: for example, low, medium, high, or unknown credit rating.

There are Different classification techniques:

1)Naive Bayes Classifier:

Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes\' theorem with strong (naive) independence assumptions between the features.

Bayes theorem provides a way of calculating the posterior probability, P(c|x), from P(c), P(x), and P(x|c). Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class conditional independence.

2)Decision tree

A decision tree is a structure that includes a root node, branches, and leaf nodes. Each internal node denotes a test on an attribute, each branch denotes the outcome of a test, and each leaf node holds a class label. The topmost node in the tree is the root node.It uses algorithm such as ID3,CART etc.

B)Clustering:

Clustering is the method by which like records are grouped together. Usually this is done to give the end user a high level view of what is going on.

Clusterings can be distinguished as:

There is algorithm K-nearest neighbour which is clustering algorithm.It is unsupervised Leraning model.

C)Association:

Association (or relation) is probably the better known and most familiar and straightforward data mining technique. Here, you make a simple correlation between two or more items, often of the same type to identify patterns. For example, when tracking people\'s buying habits, you might identify that a customer always buys cream when they buy strawberries, and therefore suggest that the next time that they buy strawberries they might also want to buy cream.

D)Neural network:

A Neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technologies.A neural network usually involves a large number of processors operating in parallel and arranged in tiers. The first tier receives the raw input information -- analogous to optic nerves in human visual processing. Each successive tier receives the output from the tier preceding it, rather than from the raw input -- in the same way neurons further from the optic nerve receive signals from those closer to it. The last tier produces the output of the system.

E)Prediction:

he prediction, as its name implied, is one of a data mining techniques that discovers the relationship between independent variables and relationship between dependent and independent variables. For instance, the prediction analysis technique can be used in the sale to predict profit for the future if we consider the sale is an independent variable, profit could be a dependent variable. Then based on the historical sale and profit data, we can draw a fitted regression curve that is used for profit prediction.

Neural network are trending today.These belongs to deep learning techniques.It tries to simulate human brain and also gives accurate answer.Hence Neural network must be used