6 Methods of Data Transformation in Data Mining

Data stands as a pivotal factor for organizational success today. Data science has emerged as a top-rated field, attracting companies to enlist data scientists for deciphering business data through data mining. Data mining unveils concealed insights from company databases. 

However, the unstructured nature of much of this data can pose challenges in comprehension, necessitating conversion into an analyzable format. To address this, data professionals employ data transformation tools. 

In this article, I’ll delve into the various methods of data transformation in data mining. But before that, let’s clarify what data mining entails. 

What is Data Mining?

Data mining is the method of analyzing data to determine patterns, correlations and anomalies in datasets. Also called the knowledge discovery process, data mining involves the extraction of valuable data and analyzing useful patterns from large databases. 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.

 The data mining process usually involves three steps – exploration, pattern identification, and deployment.

• Exploration – Data exploration is the first step of data mining. It is a process in which data analysts clean and transform data and use various data visualization techniques to extract important variables. This step is also essential to understand the nature and characteristics of data. It helps analysts visualize data and classify variables before extracting relevant data for analysis.
• Pattern Identification – Once data analysts comprehensively view data through exploration, they use automated techniques to classify data further. This is done through pattern identification. As the name suggests, pattern identification is a process that helps identify important data trends, which help organizations prepare strategies to enhance their growth. Analyzing new trends and identifying patterns also allows organizations to make future predictions. 
• Deployment – Deployment is the final stage of data mining. It involves presenting and making use of data mining results. These results are used within a targeted environment. Some examples of deployment in data mining are preparing reports, flow charts, or implementing a repeatable data mining process. 

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.

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Data are rapidly transforming every sector. Whether it is finance, health, education, science, engineering, or business, nearly all fields require valuable data to make progress. This is done with the help of data mining.

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.

What is Data Transformation?

Data mining can be complex due to the ocean of data available in various sectors. To ease the data mining process and make it more effective, the data transformation process is carried out to categorize data so that it can be done smoothly. This is also termed data preprocessing. It changes data format or values to make it more significant and allows data mining models to access valuable data easily. Data transformation enhances the quality of data in a dataset and helps eliminate null values, duplicated information, incompatible formats, and wrong indexing.

Data Transformation in Data Preprocessing

Data transformation in data preprocessing is an essential step in the data mining process. It forms an integral part of data mining, enabling analysts to sieve through the most complex datasets and retrieve insights.

Types of Data Transformation

Here are some of the most common types of data transformation processes that make the data mining process less complex:

• Bucketing/Binning:- It is the process of arranging or breaking data into different ranges called buckets. It makes data more structured and mitigates the risk of minor observational errors. This type of data transformation in data preprocessing uses various thresholds to convert numerical data into categorical data by arranging them in different buckets.
• Format Revision:- One of the major problems in data mining is processing different types of data in a particular set. This issue is solved with the help of the format revision type of data transformation.  This process standardizes data by converting all information into a consistent format.
• Data Splitting:- This is another important data transformation in data preprocessing method. It breaks down or splits data from a single column into multiple columns for training, testing or experimental purposes.

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.

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

Data Smoothing

Data smoothing is the first type of data transformation technique. 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. It is a statistical process that removes outliers from data with the help of an algorithm, making it easier to notice and predict patterns in a dataset. In simple words, data smoothing is the process of removing redundant, distorted, or meaningless data from a dataset. When the noise gets removed from the dataset, analysts can identify and predict useful data trends.

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

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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. Data aggregation is data transformation in data preprocessing technique. It is an important process that helps track and analyzes user behavior. Data aggregation is one of the most crucial steps for businesses as it streamlines the process of analyzing business schemes. This type of data transformation method is used when a dataset has large amounts of irrelevant information. It neatly summarizes signify data which enhances user experience and facilitates behavior analysis.

Data aggregation method is carried out with the help of data aggregators, a system that enables data collection from a variety of sources, processing, and storing it in a summarized manner. 

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.

There are two types of data aggregation in data mining – time aggregation and spatial aggregation. Time aggregation provides a data point for a single resource whereas spatial aggregation provides data points for a group of resources.

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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

Variable Transformation in Data Mining

Data mining process involves a lot of variables that act as placeholders for data. It is a type of data that is acquired with the help of measurements. Some examples of variables include length, time, and temperature. These are used to make predictions during the data mining process by adding different values to each variable. Data mining processes often involve variable transformation. It is an operation that facilitates changing the measurement scale of a variable. The main purpose of variable transformation in data mining is to make the data model perform better. It can also be done to make assumptions about certain data trends or patterns, or remove outliers from a dataset.

There are mainly two types of variables – numerical and categorical. When one numerical variable is transformed into another numerical variable by changing the values of a variable, it is termed numerical variable transformation. Categorical variable transformation, on the other hand, is the process of transforming a categorical variable into a numeric variable.

Wrapping up

Data transformation techniques in data mining play a crucial role in crafting usable datasets and executing operations like lookups, timestamp additions, and geolocation integration. Companies leverage Python or SQL code scripts, or opt for cloud-based ETL (extract, transform, load) tools for this purpose. I hope the methods of data transformation in data mining outlined in this article will help you how data transformation occurs. 

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