Data Transformation in Data Mining: An Easy Guide(2021)

Introduction

Every organization gathers and stores all types of data related to its functions, products, services, feedback and more. The data often sits without a framework to interpret it or tools to convert it from its raw format to one where useful information can be extracted. This is where data mining and data transformation in data mining comes in to picture.

In this article let us look at:

1. What is Data Mining
2. Applications of Data Mining
3. Data Transformation in Data Mining: The Processes

1. What is Data Mining 

Data mining is the process of analyzing tremendous amounts of data to gather business intelligence that can help organizations solve existing problems, seize new opportunities or mitigate risks. 

With data mining, it is possible to determine patterns, anomalies or correlations in datasets that are sourced from places such as financial information, employee databases, vendor lists, network traffic, client databases and consumer accounts. Data from sources is always gathered in one form or another which cannot be used immediately to glean any insight into the necessary field. The data has to first be converted into a readable format by using data transformation in data mining. The transformed data can be visualized or represented in numerous other ways that serve business intelligence. 

2. Applications of Data Mining 

Data mining is mainly used in industries that have strong consumer demand and generate enormous amounts of data. Here are some examples: 

• Healthcare: Data mining in the healthcare industry has the potential to greatly improve the industry. Data mining approaches like Machine learning, statistics and data visualization are used by analysts to forecast patterns or predict future illnesses. It can also recognize and stop fraud. 
• Market basket analysis: This modeling method uses a hypothesis that consumers who buy a certain set of products are more likely to buy another group of items. It enables retailers to quantify purchasing behaviors and present the right sets to individuals or demographics. 
• Education: Data mining in education is a relatively new field and is largely concerned with using data generated from educational environments to explore the knowledge and learning patterns of students. With learning from data, insight institutions can tailor education towards their students. 
• Manufacturing engineering: Knowledge of the various factors that determine product success is critical for manufacturing companies. Data mining can be used to forecast product development, customer expectations, cost and other tasks. 
• Customer Relationship Management: Maintaining a good customer relationship comes down to understanding the various preferences, emotions and pain points of customers. Using data mining, it is possible to enhance customer loyalty and implement strategies that are oriented to keep them happy and to continue the business. 
• Fraud detection: With market fraud rising every year data mining is becoming the go-to approach as traditional methods of fraud detection are either inefficient or not good enough. Meaningful patterns from behaviors of consumers and organizations can be extracted from data mining and applied to detect anomalies activities or fraud in all industries. 
• Lie detection: Similar to the detection of fraud, lie detection has far-reaching consequences starting from the basic domestic criminal to geopolitics. Social media posts, text messages and other forms of voice and mail communications can be mined to monitor criminal behavior or terroristic threats. 
• Financial banking: The financial sector generates tremendous amounts of data that can be used by banking and other services to identify trends, market costs and other business-relevant information. 

3. Data Transformation in Data Mining: The Processes

The data transformation in data mining is accomplished using a combination of structured and unstructured data. It is transferred to a cloud data warehouse and arranged homogeneously to make it easier to recognize patterns. Here are the steps involved: 

• Smoothing: Smoothing is a process used to remove the unnecessary, corrupt or meaningless data or ‘noise’ in a dataset. Smoothing improves the algorithm’s ability to detect useful patterns in data. 
• Aggregation: Data aggregation is gathering data from a number of sources and storing it in a single format. Aggregation, in itself, is a process of improving the quality of the data where it helps gather info about data clusters and collect lots of data. 
• Discretization: Discretization is one of the transformation methods that break up continuous data into small intervals. Although data mining requires continuous data, the existing frameworks can only handle discrete data chunks.
• Attribute construction: In attribute construction, new attributes are generated and applied in the mining process from the existing set of attributes. It improves mining efficiency by simplifying the original data. 
• Generalization: Generalization is used to convert low-level data attributes to high-level data attributes by the use of concept hierarchy. An example is an age in the numerical form of raw data (22, 52) is converted into (Young, old) categorical value. 
• Normalization: Normalization is an important step in data transformation and also called pre-processing. Here the data is transformed to categorize it under a given range. 

Conclusion

As organizations need to process through large amounts of incoming data with various attributes, they have to distill the raw data form into understandable and actionable insights. The data transformation in data mining is critical in developing usable datasets to perform various operations. 

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