Posted Date: 11/01/2019
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Important Three Techniques of Data Mining

Data mining, also known as Knowledge Discovery in Data (KDD) is about hunting large stores of data to unearth patterns and modes that go beyond simple analysis. This, however, is not a one-stop solution but a multi-step process and accomplished in various stages. 

Let us examine some data mining methods in brief. According to Top Data Mining Companies, most of the organizations combine two or more data mining methods together to form a relevant process that meets their business needs.

Association Rule Learning

Association rule learning authorizes the detection of interesting relations (interdependencies) between different variables in large databases. Association rule learning unearths hidden patterns in the data that can be utilized to recognize variables within the data and the co-occurrences of different variables that appear with the greatest prevalence.

Association rule learning is often utilized in the retail industry to hunt patterns in point-of-sales data. These patterns can be utilized when suggesting new products to others based on what others have bought before or based on which products are bought together. If this is done accurately, it can guide organisations enhance their conversion rate.

Classification 

This data mining technique differs since it is based on machine learning and utilizes mathematical techniques such as Linear programming, Decision trees, Neural network. In classification, companies try to develop software that can grasp how to categorize the data items into groups. For example, a company can describe a classification in the application that “given all records of candidates who provided to resign from the company, forecast the number of individuals who are likely to resign from the company in future.” Under such a situation, the company can categorize the records of employees into two groups that namely “leave” and “stay”. It can then use its data mining software to categorize the candidates into separate groups created earlier.

Clustering Analysis

Clustering analysis is the process of pinpointing data sets that are similar to each other, to comprehend the differences as well as resemblances within the data. Clusters have certain traits in common that can be utilized to revamp targeting algorithms. For instance, clusters of customers with similar buying behaviour can be aimed with similar products and services to enhance the conversation rate.

A result from a clustering analysis can be the formation of personas. Personas are fictional characters created to render the different user types within a targeted demographic, attitude and/or behaviour set that might utilize a site, brand or product in a similar way.

With data mining services, BDS Services allow businesses to make proactive, knowledge-driven decisions by obtaining knowledge about your customer behaviour towards your business offerings.


Prashant Thakur is a content writer eager to become a pro content marketer. Passionate about words, texts, and literature. Wonderer and dreamer.


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