Introduction to Data Mining Techniques
In this Topic, we will learn about Data mining Techniques; As the advancement in the field of Information, technology has led to a large number of databases in various areas. As a result, there is a need to store and manipulate important data that can be used later for decision-making and improving the activities of the business.
What is Data Mining?
It is the process of extracting useful information and patterns from enormous data. It includes collection, extraction, analysis, and statistics of data. It is also known as the Knowledge discovery process, Knowledge Mining from Data or data/ pattern analysis. It is a logical process of finding useful information to find out useful data. Once the intake and designs are found, it can be used to develop the business. Data mining tools can answer various questions related to your business which was too difficult to resolve. They also forecast the future trends which let the business people 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 information 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
Following are the benefits given.
- Automated prediction of trends and behaviors
- It can be implemented on new systems as well as existing platforms.
- It can analyze huge databases in minutes.
- Automated discovery of hidden patterns
- There are a lot of models available to understand complex data quickly.
- It is of high speed, making it easy for the users to analyze a huge amount of data in less time.
- It yields improved predictions.
List of 7 Important Data Mining Techniques
One of the most important tasks 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 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 that are discussed in this article. There are also many 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 that relates to the collection and description of data. The 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 analysts should possess some knowledge about the different statistical techniques. In today’s world, people have to deal with many data and derive important patterns from it. Statistics can help you to a greater extent to get answers to questions about their data like
- What are the ways 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 answer 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 make smart decisions. There are different forms of statistics, but the most important and useful technique is collecting and counting data. There are a lot of ways to collect data like
- Histogram
- Mean
- Median
- Mode
- Variance
- Max
- Min
- Linear Regression
2. Clustering Technique
Clustering is one of 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 allows 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 the Nearest Neighbour. The nearest neighbor technique is very similar to clustering. It is a prediction technique to predict what an estimated value is in one record look for records with similar estimated values in a historical database and use the prediction value from the form near the unclassified document. This technique states that the objects which are closer to each other will have similar prediction values. Through this method, you can easily predict the importance of the nearest items very quickly. Nearest Neighbour is the easiest to use the technique because they work as per the people’s thoughts. 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.
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In business, the 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 impressive.
3. 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 types of research are going on these days to produce an interesting projection of databases called Projection Pursuit. There is a lot of data mining technique which will have useful patterns for good data. But visualization is a technique that converts Poor data into useful data letting different kinds of Data Mining methods to be used in discovering hidden patterns.
4. 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. The leaves of the trees are considered partitions of the dataset related to that particular classification. This technique can be used for exploration analysis, data pre-processing, and prediction work.
The decision tree can be considered segmentation of the original dataset where segmentation is done for a particular reason. Each data that comes under a segment has some similarities in the information being predicted. Decision trees provide results that the user can easily understand.
Statisticians mostly use the decision tree technique to find out which database is more related to the business’s problem. The 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 tree branch. 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 that picks the questions more complexly. 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 details, it again asks questions on each of the new element 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.
5. Neural Network
Neural Network is another important technique used by people these days. This technique is most often used in the starting stages of data mining technology. The artificial neural network was formed out of the community of Artificial intelligence.
Neural networks are straightforward to use as they are automated to a particular extent. 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 are the nodes connected?
- How many processing units to be used?
- When should the training process 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, forming a single layer or multiple layers. The formation of neurons and their interconnections are called the architecture of the network. There are many neural network models, and each model has its own advantages and disadvantages. Every neural network model has different architectures, and these architectures use other learning procedures.
Neural networks are a powerful predictive modeling technique. But it is not very easy to understand even by experts. It creates very complex models that are impossible to understand fully. Thus to understand the Neural network technique companies are finding out new solutions. Two solutions have already been suggested.
- The first solution is Neural network is packaged up into a complete solution that will let it be used for a single application.
- The second solution is it is bonded with expert consulting services.
The neural network has been used in various kinds of applications. This has been used in the business to detect frauds taking place in the industry.
6. 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 used to identify the variables and the frequent occurrence of other variables with the highest frequencies.
Association rule offers two primary pieces of 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 rules. They are
- Multilevel Association Rule
- Multidimensional Association Rule
- Quantitative Association Rule
This technique is most often used in the retail industry to find patterns in sales. This will help increase the conversion rate and thus increases profit.
7. Classification
Data mining techniques classification is the most commonly used data mining technique with a set of pre-classified samples to create a model that can classify a large group of data. This technique helps in deriving important information about data and metadata (data about data). This technique is closely related to the cluster analysis technique, and it uses the decision tree or neural network system. There are two main processes involved in this technique
- Learning – In this process the data are analyzed by the 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 a classification technique is Email provider.
Conclusion
From this article, we have known the important Data Mining techniques. And the characteristics and specifications of each of the techniques are explained in detail. It is an important tool in many areas of business, and the techniques are best used in deriving a solution to a problem. Therefore, companies must use these techniques to help business people make smart decisions. No single method can be used to solve the problem in business. All the data mining techniques should go hand in hand to solve an issue.
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