Importance Of Data Transformation In Data Mining
It is a known fact that data is one of the important parts of any organization and there are numerous enticing job opportunities in the sector. But there is one issue, and that is- most of the data collected from the original source are unstructured and difficult to understand which need to be converted into a simple format (i.e. data transformation in data mining) and managed by cloud-based ETL tools for an accurate analysis.
This is where the concept of data mining comes into play. It refers to the process of finding 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 data transformation is data mining 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 such as smoothing, clustering, binning, regression, histogram, etc. are included.
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 develop better strategies to enhance customer acquisition and decrease or increase cost and revenue and many more. It uses statistics, and 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 to the dataset in these processes.
- Z-Score Normalization: This data transformation in data mining technique is used to normalize the value for attributes 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.
Ways of Data Transformation in Data Mining
There are several ways of carrying out data transformation in data mining, like Scripting, On-Premises ETL Tools, and Cloud-Based ETL Tools. Following is a brief explanation for them:
- Scripting: It involves data transformation (in data mining) through scripting 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 (in data mining) 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. Using these 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.
Conclusion
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. Data transformation in data mining is also critical to predicting multiple things and is required as per today's needs where data is everything.
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