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 many databases in various areas. As a result, storing and manipulating important data that can be used later for decision-making and improving business activities is necessary.
What is Data Mining?
It is the process of extracting useful information and patterns from enormous amounts of data. It includes collection, extraction, analysis, and statistics of data. It is a logical process of finding useful information to find valuable data. Data mining tools can answer various questions related to your business which took a lot of work to resolve. They also forecast future trends, which lets 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 to make the best prediction.
- Deployment – The identified patterns are used to get the desired outcome.
Benefits of Data Mining
- Automated prediction of trends and behaviors
- It can analyze large databases in minutes.
- Automatic 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 users to analyze a vast amount of data in less time.
- It yields improved predictions.
List of 7 Important Data Mining Techniques
One of the most critical tasks in Data Mining is to select the correct data mining technique. There are also many other Data Mining techniques, but business people more frequently use these seven.
- 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. Many analysts do not consider the statistical technique a data mining technique. But still, it helps to discover the patterns and build predictive models. For this reason, data analysts should know about the different statistical methods. Today, people must deal with and derive important patterns from many data. 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 beneficial 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, but they also help in summarizing the data and counting it. It also helps in providing information about the data with ease. Through statistical reports, people can make intelligent decisions. There are different forms of statistics, but the most critical and proper 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 analysis is the process of identifying similar data. This will help to understand the differences and similarities between the data. 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 papers 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 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 technique because they work according to people’s thoughts. They also work very well in terms of automation. They perform complex ROI calculations with ease. This technique’s accuracy level is as good as the other Data Mining techniques.
3. Visualization
Visualization is the most helpful technology which is used to discover data patterns. Many types of research are going on these days to produce an exciting projection of databases called Projection Pursuit. There is a lot of data mining technique that will have valuable patterns for good data.
4. Induction Decision Tree Technique
A decision tree is a predictive model; the name implies it looks like a tree. Each data that comes under a segment has similarities in the predicted information. Decision trees provide results that the user can easily understand.
Statisticians mostly use the decision tree technique to determine which database relates more to the business’s problem.
The first and foremost step in this technique is growing the tree. The decision tree stops growing under any one of the below circumstances.
- If the segment contains only one record
- All the records have 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. After deciding on the details, it again asks questions on each 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 selecting the splits.
5. Neural Network
A 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.
But to make the neural network work efficiently, you need to know.
- How are the nodes connected?
- How many processing units are to be used?
- When should the training process be stopped?
This technique has two main parts – the node and the link.
- The node – which freely matches the neuron in the human brain
- The link – which freely matches the connections between the neurons in the human brain
A neural network is a collection of interconnected neurons, forming single or multiple layers. There are many neural network models, each with 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, companies are finding new solutions to understand the Neural network technique. Two solutions have already been suggested.
- The first solution is a Neural network 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.
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.
The association rule offers two primary pieces of information.
- Support – Hoe often is the rule applied?
- Confidence – How often the government 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 will help increase the conversion rate and thus increases profit.
7. Classification
This technique helps derive meaningful information about data and metadata (data about data). This technique is closely related to the cluster analysis technique, using 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 critical Data Mining techniques. Therefore, companies must use these techniques to help business people make intelligent decisions. All the data mining techniques should go hand in hand to solve an issue.
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