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Data Mining Techniques

Data Mining is the process of extracting useful information and patterns from enormous data. Data Mining includes collection, extraction, analysis and statistics of data. It is also known as Knowledge discovery process, Knowledge Mining from Data or data/ pattern analysis. Data Mining is a logical process of finding useful information to find out useful data. Once the information and patterns are found it can be used to make decisions for developing the business. Data mining tools can give answers to your various questions related to your business which was too difficult to resolve. They also forecast the future trends which lets the business people to make proactive decisions.

Data mining involves three steps. They are

  • Exploration– In this step the data is cleared and converted into another form. The nature of data is also determined
  • Pattern Identification– The next step is to choose the pattern which will make the best prediction
  • Deployment– The identified patterns are used to get the desired outcome.

Benefits of Data Mining

  • Automated prediction of trends and behaviours
  • It can be implemented on new systems as well as existing platforms
  • It can analyze huge database in minutes
  • Automated discovery of hidden patterns
  • There are a lot of models available to understand complex data easily
  • It is of high speed which makes it easy for the users to analyze huge amount of data in less time
  • It yields improved predictions

Data Mining Techniques

One of the most important task in Data Mining is to select the correct data mining technique. Data Mining technique has to be chosen based on the type of business and the type of problem your business faces. A generalized approach has to be used to improve the accuracy and cost effectiveness of using data mining techniques. There are basically seven main Data Mining techniques which is discussed in this article. There are also a lot of other Data Mining techniques but these seven are considered more frequently used by business people.

  • Statistics
  • Clustering
  • Visualization
  • Decision Tree
  • Association Rules
  • Neural Networks
  • Classification
  1. Statistical Techniques

Data mining techniques statistics is a branch of mathematics which relates to the collection and description of data. Statistical technique is not considered as a data mining technique by many analysts. But still it helps to discover the patterns and build predictive models. For this reason data analyst should possess some knowledge about the different statistical techniques. In today’s world people have to deal with large amount of data and derive important patterns from it. Statistics can help you to a greater extent to get answers for questions about their data like

  • What are the patterns in their database ?
  • What is the probability of an event to occur ?
  • Which patterns are more useful to the business ?
  • What is the high level summary that can give you a detailed view of what is there in the database ?

Statistics not only answers these questions they help in summarizing the data and count it. It also helps in providing information about the data with ease. Through statistical reports people can take smart decisions. There are different forms of statistics but the most important and useful technique is the collection and counting of data. There are a lot of ways to collect data like

  • Histogram
  • Mean
  • Median
  • Mode
  • Variance
  • Max
  • Min
  • Linear Regression
  1. Clustering Technique

Clustering is one among the oldest techniques used in Data Mining. Clustering analysis is the process of identifying data that are similar to each other. This will help to understand the differences and similarities between the data. This is sometimes called segmentation and helps the users to understand what is going on within the database. For example, an insurance company can group its customers based on their income, age, nature of policy and type of claims.

There are different types of clustering methods. They are as follows

  • Partitioning Methods
  • Hierarchical Agglomerative methods
  • Density Based Methods
  • Grid Based Methods
  • Model Based Methods

The most popular clustering algorithm is Nearest Neighbour. Nearest neighbour technique is very similar to clustering. It is a prediction technique where in order to predict what a estimated value is in one record look for records with similar estimated values in historical database and use the prediction value from the record which is near to the unclassified record. This technique simply states that the objects which are closer to each other will have similar prediction values. Through this method you can easily predict the values of nearest objects very easily. Nearest Neighbour is the most easy to use technique because they work as per the thought of the people. They also work very well in terms of automation. They perform complex ROI calculations with ease. The level of accuracy in this technique is as good as the other Data Mining techniques.

In business Nearest Neighbour technique is most often used in the process of Text Retrieval. They are used to find the documents that share the important characteristics with that main document that have been marked as interesting.

  1. Visualization

Visualization is the most useful technique which is used to discover data patterns. This technique is used at the beginning of the Data Mining process. Many researches are going on these days to produce interesting projection of databases, which is called Projection Pursuit. There are a lot of data mining technique which will produce useful patterns for good data. But visualization is a technique which converts Poor data into good data letting different kinds of Data Mining methods to be used in discovering hidden patterns.

  1. Induction Decision Tree Technique

A decision tree is a predictive model and the name itself implies that it looks like a tree. In this technique, each branch of the tree is viewed as a classification question and the leaves of the trees are considered as partitions of the dataset related to that particular classification. This technique can be used for exploration analysis, data pre-processing and prediction work.

Decision tree can be considered as a segmentation of the original dataset where segmentation is done for a particular reason. Each data that comes under a segment has some similarities in their information being predicted. Decision trees provides results that can be easily understood by the user.

Decision tree technique is mostly used by statisticians to find out which database is more related to the problem of the business. Decision tree technique can be used for Prediction and Data pre-processing.

The first and foremost step in this technique is growing the tree. The basic of growing the tree depends on finding the best possible question to be asked at each branch of the tree. The decision tree stops growing under any one of the below circumstances

  • If the segment contains only one record
  • All the records contain identical features
  • The growth is not enough to make any further spilt

CART which stands for Classification and Regression Trees is a data exploration and prediction algorithm which picks the questions in a more complex way. It tries them all and then selects one best question which is used to split the data into two or more segments. After deciding on the segments it again asks questions on each of the new segment individually.

Another popular decision tree technology is CHAID (Chi-Square Automatic Interaction Detector). It is similar to CART but it differs in one way. CART helps in choosing the best questions whereas CHAID helps in choosing the splits.

  1. Neural Network

Neural Network is another important technique used by people these days. This technique is most often used in the starting stages of the data mining technology. Artificial neural network was formed out of the community of Artificial intelligence.

Neural networks are very easy to use as they are automated to a particular extent and because of this the user is not expected to have much knowledge about the work or database. But to make the neural network work efficiently you need to know

  • How the nodes are connected ?
  • How many processing units to be used ?
  • When should the training process to be stopped ?

There are two main parts of this technique – the node and the link

  • The node– which freely matches to the neuron in the human brain
  • The link– which freely matches to the connections between the neurons in the human brain

A neural network is a collection of interconnected neurons. which could form a single layer or multiple layer. The formation of neurons and their interconnections are called architecture of the network. There are a wide variety of neural network models and each model has its own advantages and disadvantages. Every neural network model has different architectures and these architectures use different learning procedures.

Neural networks are very strong predictive modelling technique. But it is not very easy to understand even by experts. It creates very complex models which is impossible to understand fully. Thus to understand the Neural network technique companies are finding out new solutions. Two solutions have already been suggested

  • First solution is Neural network is packaged up into a complete solution which will let it to be used for a single application
  • Second solution is it is bonded with expert consulting services

Neural network has been used in various kinds of applications. This has been used in the business to detect frauds taking place in the business.

  1. Association Rule Technique

This technique helps to find the association between two or more items. It helps to know the relations between the different variables in databases. It discovers the hidden patterns in the data sets which is used to identify the variables and the frequent occurrence of different variables that appear with the highest frequencies.

Association rule offers two major information

  • Support– Hoe often is the rule applied ?
  • Confidence– How often the rule is correct ?

This technique follows a two step process

  • Find all the frequently occurring data sets
  • Create strong association rules from the frequent data sets

There are three types of association rule. They are

  • Multilevel Association Rule
  • Multidimensional Association Rule
  • Quantitative Association Rule

This technique is most often used in retail industry to find patterns in sales. This will help increase the conversion rate and thus increases profit.

  1. Classification

Data mining techniques classification is the most commonly used data mining technique which contains a set of pre classified samples to create a model which can classify the large set of data. This technique helps in deriving important information about data and metadata (data about data). This technique is closely related to cluster analysis technique and it uses decision tree or neural network system. There are two main processes involved in this technique

  • Learning– In this process the data are analyzed by classification algorithm
  • Classification– In this process the data is used to measure the precision of the classification rules

There are different types of classification models. They are as follows

  • Classification by decision tree induction
  • Bayesian Classification
  • Neural Networks
  • Support Vector Machines (SVM)
  • Classification Based on Associations

One good example of classification technique is Email provider.

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