Data Mining Tools and Techniques

Data mining is the process of extraction of knowledge from raw data to perform predictive analytics and operations. Most companies use its different approaches to observe trends in data.

The DM algorithms learn from the data and are able to answer various business oriented questions by getting the information hidden inside the humongous amounts of data. The key objective of data mining is to model the systems to discover relationships to connect variables in a given database.

The discovered knowledge is then put into a decision support system that helps management to take important business decisions for an organisation.  

Definition of Data Mining  

Data Mining is the process of extracting valid and previously unknown, actionable and complete data from large databases and using it to make very important business decisions. 

DM uses a discovery model to search for frequently occurring patterns and trends to make a generalization about the database. The user has very little contribution in working on the model. Another purpose of data mining is to get a high yield of usable facts from the data in the fastest time.

Data Mining process 

These are the basic steps :

  • Collection and preparation of data that comes from a data warehouse 
  • Search for patterns by using various techniques such as predictive modeling, database segmentation, link analysis, and Deviation detection. 
  • Review the Results 
  • Report and refine results 

Techniques 

Various effective data mining techniques can be applied to your data for extracting patterns for prediction. Some of the most popularly used techniques include predictive modeling, data segmentation, link analysis among many others. Various business rules can also be applied by using these methods. These techniques can be used to solve various tasks such as :

  1. Predicting: Involves learning and patterns and predicts future values of new target variables. 
  2. Clustering: Involves identifying categories to form clusters of similar property data.
  3. Classification: Involves the mapping of data into various discrete classes.
  4. Deviation Detection: Involves the process of identifying changes in key attributes of a dataset.
  5. Relationship detection: Involves the process of determining relationships in data to understand the relevance of data attributes. 

Tools 

Various Commercial and open-source data mining tools are available for developing models in data mining. Before opting for a particular data mining tool, we must check that each data mining tool must include the following features:

  • Data Preparation and cleansing 
  • Data Mining algorithm selection 
  • Provides Product Scalability, high performance and parallel processing
  • Effective options for understanding the results obtained

Related Technologies and Applications 

Some very useful technologies used in data mining are listed below: 

Genetic Algorithms 

Evolutionary algorithms are analogous to the evolutionary theory. It consists of various optimization techniques that use processes like mutation, natural selection as well as combinations of genes to get the best offspring (information in our case). 

Neural Networks 

Neural Networks are analogous to human neural networks to train and predict data. It is very effective and is used in deploying Deep Learning algorithms. 

Rule based Reasoning and Induction 

It uses if-then rules based on its statistical importance to extract information from the database in the problem. 

Case-based reasoning 

Also known as the nearest neighbor method, this method involves solving real-world problems by using a case or past experience that is relevant to the data mining problem in question

Decision Tree 

A decision tree is a tree structure to make important decisions and predictions in machine learning and data science. Useful in the classification of a data set. 

Advantages of DM

Some crucial advantages are listed below:

  1. Generation of elevated business opportunities by automation in prediction 
  2. Faster trend analysis
  3. Ideal for large databases 
  4. Searches valuable business information by using automated processes to identify unknown patterns in data 

DM is very important for analysts and experts to extract valuable information from a provided database. It is a combination of various approaches and actions which makes it a varied approach that can solve plenty of data problems in crucial real-world applications. 

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