6 Methods of Data Transformation in Data Mining

Data is currently one of the most important ingredients for success for any modern-day organization. With data science being rated among the most exciting fields to work, companies are hiring data scientists to make sense of their business data. These data professionals use a process called data mining to uncover hidden information from the company databases.

But, as most of this data is unstructured, it might be difficult to understand. It needs to be converted into a format that is easier to analyze. For this, the techies use data transformation tools.

In this article, we will learn about the different methods of data transformation in data mining. But first, let us see what data mining means.

What is Data Mining?

Data mining is the method of analyzing data to determine patterns, correlations and anomalies in datasets. These datasets consist of data sourced from employee databases, financial information, vendor lists, client databases, network traffic and customer accounts. Using statistics, machine learning (ML) and artificial intelligence (AI), huge datasets can be explored manually or automatically.

Data mining helps companies develop better business strategies, enhance customer relationships, decrease costs and increase revenues.

In the data mining process, the business goal that is to be achieved using the data is determined first. Data is then collected from various sources and loaded into data warehouses, which is a repository of analytical data. Further, data is cleansed – missing data is added and duplicate data is removed. Sophisticated tools and mathematical models are used to find patterns within the data.

The results are compared with the business objectives to see whether it can be used for business operations. Based on the comparison, the data is deployed within the company. It is then presented using easy to understand graphs or tables.

Applications of Data Mining

Data mining is used in several sectors:

• Multimedia companies use data mining to understand consumer behaviour and launch appropriate campaigns.
• Financial firms use it to understand market risks, detect financial frauds and get the best investment returns.
• In retail companies, data mining is used for understanding customer demands, their behaviour, forecast sales, and launch more targeted ad campaigns through data models.
• Manufacturing industries use data mining tools to manage their supply chain, improve quality assurance, and use machine data to predict machinery defects that help in the maintenance.
• Data mining is used to upgrade security systems, detect intrusions and malware. Data mining software can be used to analyze e-mails and filter out spam from your e-mail accounts.

Data Transformation in Data Mining: The Processes

Data transformation in data mining is done for combining unstructured data with structured data to analyze it later. It is also important when the data is transferred to a new cloud data warehouse. When the data is homogeneous and well-structured, it is easier to analyze and look for patterns.

For example, a company has acquired another firm and now has to consolidate all the business data. The smaller company may be using a different database than the parent firm. Also, the data in these databases may have unique IDs, keys and values. All this needs to be formatted so that all the records are similar and can be evaluated.

This is why data transformation methods are applied. And, they are described below:

Data Smoothing

This method is used for removing the noise from a dataset. Noise is referred to as the distorted and meaningless data within a dataset. Smoothing uses algorithms to highlight the special features in the data. After removing noise, the process can detect any small changes to the data to detect special patterns.

Any data modification or trend can be identified by this method.

Data Aggregation

Aggregation is the process of collecting data from a variety of sources and storing it in a single format. Here, data is collected, stored, analyzed and presented in a report or summary format. It helps in gathering more information about a particular data cluster. The method helps in collecting vast amounts of data.

This is a crucial step as accuracy and quantity of data is important for proper analysis. Companies collect data about their website visitors. This gives them an idea about customer demographics and behaviour metrics. This aggregated data assists them in designing personalized messages, offers and discounts.

Discretization

This is a process of converting continuous data into a set of data intervals. Continuous attribute values are substituted by small interval labels. This makes the data easier to study and analyze. If a continuous attribute is handled by a data mining task, then its discrete values can be replaced by constant quality attributes. This improves the efficiency of the task.

This method is also called data reduction mechanism as it transforms a large dataset into a set of categorical data. Discretization also uses decision tree-based algorithms to produce short, compact and accurate results when using discrete values.

Generalization

In this process, low-level data attributes are transformed into high-level data attributes using concept hierarchies. This conversion from a lower level to a higher conceptual level is useful to get a clearer picture of the data. For example, age data can be in the form of (20, 30) in a dataset. It is transformed into a higher conceptual level into a categorical value (young, old).

Data generalization can be divided into two approaches – data cube process (OLAP) and attribute oriented induction approach (AOI).

Attribute construction

In the attribute construction method, new attributes are created from an existing set of attributes. For example, in a dataset of employee information, the attributes can be employee name, employee ID and address. These attributes can be used to construct another dataset that contains information about the employees who have joined in the year 2019 only.

This method of reconstruction makes mining more efficient and helps in creating new datasets quickly.

Normalization

Also called data pre-processing, this is one of the crucial techniques for data transformation in data mining. Here, the data is transformed so that it falls under a given range. When attributes are on different ranges or scales, data modelling and mining can be difficult. Normalization helps in applying data mining algorithms and extracting data faster.

The popular normalization methods are:

• Min-max normalization
• Decimal scaling
• Z-score normalization

Wrapping up

The techniques of data transformation in data mining are important for developing a usable dataset and performing operations, such as lookups, adding timestamps and including geolocation information. Companies use code scripts written in Python or SQL or cloud-based ETL (extract, transform, load) tools for data transformation.      

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