Importance Of Data Transformation In Data Mining

  • What is a Data Mining?
  • Applications of Data Mining
  • What is Data Transformation?
  • Data Transformation Techniques
  • Ways of Data Transformation

Currently, data is one of the important parts of any organization but most of the data collected from the original source are unstructured and difficult to understand which need to be converted in a simple format and managed by cloud-based ETL tools for an accurate analysis.

Here comes the concept of data mining which is a process to find data and its patterns within a large number of data sets to predict the outcome of decreased costs of an organization, annual sales, anomalies activities, etc. from it for business strategy and other individual fields.

But because of its difficulty in reading the data collected in the cloud data warehouse from the mining process, data transformation is needed.

Data transformation is a technique to process data from the source location to recognize and restructure it easily. It includes data cleaning and reduction and within it processes as smoothing, clustering, binning, regression, histogram, etc. are included.

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What is a Data Mining?

Data Mining is a method to analyze data to determine patterns, anomalies, and correlations from a data source. It can conclude the data like employee database, annual sales report, vendor lists, and even infrastructure costs, etc.

It helps organizations to developed better strategies to enhance customer acquisition and decrease or increase cost and revenue and many more. It uses statistics, ML AI to explore the dataset automatically or manually.

In the data mining process first, the raw data is collected from various original sources, then all the data are loaded in the data warehouses. It is a repository that is filled with analytical data.

Further, the data goes through various processes and mining algorithms where the same data are removed and missing data is added in the dataset in these processes.

Applications of Data Mining

Data Mining is used in several sectors of an organization:

  • Various multimedia organizations use data mining to improve the understanding the consumer behavior and launch new campaigns according to it.
  • To understand the market risks and to detect financial frauds many financial firms use data mining for these things.
  • Many Retail companies use data mining to understand customer demands and their behavior on various products and the price range and they also use it to forecast new sales prices and launch new products and much more.
  • Manufacturing companies use data mining to manage their supply chain, improve their management, predict machinery defects, product qualities, etc.
  • It is also used to upgrade security systems, find defects in computer systems, or malware in databases. It can also analyze emails to filter spam emails for companies.

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What is Data Transformation?

In Data Mining the collected data is raw. So it has to go through the entire process of transformation and it is called Data transformation. It is a technique used to convert the collected raw data into a suitable and readable format so it eases data mining in retrieving the data efficiently and quickly by using its attribute values.

Data Transformation Techniques

In Data transformation, some methods and transformation processes are required. These are as follows:

1. Data Smoothing

It is a process to remove noise from the dataset using some specific algorithm. It also helps to highlight present important features in the dataset. It allows organizations to predict the patterns. While collecting the raw data it can manipulate it to reduce or eliminate the noise or any unnecessary form of data from the dataset.

The concept behind it is that it can predict different patterns and trends in the dataset by identifying simple changes in the data set. It helps to analyze the dataset to find the patterns cause doing it manually is not possible when there is a large number of data.

These are some processes by which it can reduce the noise from the data:

  • Binning: This method is used to split the sorted data into small portions and smoothens the data value of the small portions considering the other values around it.
  • Regression: It identifies the relationship between two dependent attributes so that we can predict the other numeric attributes by using the single attribute connected to it by a specific relation.
  • Clustering: This method is used to group similar data values and create a cluster from them. The values which are outside the cluster are called outliers.

2. Data Aggregation

In this method, all the high-level data are collected or aggregated in a single format. Data aggregation is the process to store the data and present it in a summary format. The data is collected from multiple data sources to integrate with description for data analysis.

To produce relevant and more accurate results, the gathered data must be of high quality and in a large quantity. The collected data is used for many cases like taking a financial decision for a company or to create a strategy for product pricing, customer future review on the product, and also marketing purposes, etc.

For example, suppose we have a data set that contains the price of a product in every season, so by aggregating the data we can tell which time and at what price the organization is gaining profit through the product.

3. Discretization

This method is used to convert the continuous data into data intervals. In this method, the continuous attributes of the datasets are substituted by small interval labels and It helps to read, study and analyze the data more easily. To improve the efficiency of the data mining task while handling continuous attribute values, the method replaces its discrete values for attribute with constant quality attribute. We can understand this concept with the help of an example. Let’s assume that you have a dataset of ages of different people. Using this technique, these attributes can be replaced by interval labels such as age group or age type.

Data Discretization is also called a data reduction technique or the cleaning technique as it changes the large dataset into a set of categorical attribute. This method also helps to produce short, compact, and accurate results using a decision tree-based algorithm while using discrete values. This method can be also divided into two types:

  • Supervised Discretization: It is the used class information in the data set
  • Unsupervised Discretization: It is the direction where the mining process proceeds.

4. Generalization

This method uses the concept of hierarchy and it converts the low-level data attributes to the high-level data attributes. This method helps to get a clearer picture of the data. Data Generalization is divided into two approaches:

  • Data cube process (OLAP) approach.
  • Attribute-oriented induction (AOI) approach.

For example, let’s suppose a data set that includes age data in the form of numbers but by Data Generalization the higher conceptual level data can be transformed into a categorical level data like young and old.

5. Attribute Construction

In the attribute construction method, the data get values for attribute and it consults the existing attributes of the data set to construct a new data set that helps to ease data mining for these level data attributes. These new attributes are created to assist the data mining process from the existing attributes. It makes the data mining process more efficient by simplifying the source format of original data.

For example, let’s assume that you have a dataset of the measurements of the different plots, i.e., now you may have the height and the width of those different plots. So you can make new attributes called areas using those existing attributes and it will also help to create a relationship among the attributes in a data set.

6. Normalization

Data Normalization means to scale the data values to a much smaller range of numerical forms. There are multiple methods to normalize the data as mentioned below:

Suppose we have a numeric attribute A and we have values of n numbers for A attribute that are V1, V2,….Vn.

  • Min-Max Normalization: It implements a transformation process on the original data and that is a linear transformation. So now let us consider we have minA and maxA as the minimum and the maximum observed value for A and Vi is the value for A that has to be normalized.

After normalization, the min-max would give a map vi to the v”i in a smaller range. The min-max formula of normalization is given below.

  • Z-Score Normalization: This method is used to normalize the value for attribute a using mean and standard deviation. By using mean and standard deviation it refers that every process of normalization value in a dataset such that the standard deviation for attribute A is 1 and the mean of all values is 0. The formula of Z-Score Normalization is given below.
  • Decimal Scaling: It moves the decimal point in the value to normalize the value of attribute A. The decimal points depend on the maximum absolute value of A in numerical form. The decimal scaling formula is given below.

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Ways of Data Transformation

There are several ways of Data Transformation like Scripting, On-Premises ETL Tools, Cloud-Based ETL Tools and these are explained below

  • Scripting: It involves data transformation through scripting that and it uses Python and SQL language to write the code for extracting and transforming the data. These scripting languages use to automate some specific tasks in a program. They also help to extract the information from the dataset. It requires less code than other languages and that’s why it is less intensive.
  • On-Premises ETL Tools: It scripts the required work for data transformation by ETL Tools by automating the process. The On-premises ETL Tools are hosted on servers and by using this tool you can save time. For using these it often requires some extensive expertise and significant infrastructure cost.
  • Cloud-Based ETL Tools: This tool is used for non-technical users to utilize easily and this is hosted on the cloud which you can get by its name. This also helps to collect data and load it in data warehouses for analysis insights and actionable insights. By using this tool a user can choose how much data they want to pull from their source of data and also monitor its usage.

Summing Up

These days data mining is very important for multiple use cases and to also improve data collected from the data source. For data mining, the data needs to be categorized and also needs to go through some processes. By using data mining, it can predict multiple things and is required as per today’s needs where data is everything.

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