The term ‘Data Mining’ was properly introduced in the 1990s along with the other terms such as Data Warehousing, Business Intelligence (hereinafter referred to as “BI”) and analytics technologies which began to emerge in order to analyze the immense amounts of data that companies were producing and gathering. Data Mining can be traced back to Bayes’ theorem and evolutionary regression which revolves around 1700 and 1800 eras respectively.
It was first used in first international conference on Knowledge Discovery and Data Mining which was held in Montreal in 1955. Subsequently, Data Mining and Knowledge Discovery published its first technical journal containing articles, discoveries, knowledge, techniques and practices on Data Mining in 1997. Organizations began utilizing Data Mining to analyze data, identify trends, and predict changes in interest rates, stock prices, and client demand in order to increase their customer base.
WHAT IS DATA MINING?
Data Mining is typically defined as procedure of extracting information from huge sets of data. It is also defined as mining knowledge from data. Data Mining techniques are to make machine learning models that enable Artificial Intelligence (hereinafter referred to as “AI”) such as search engine algorithms and recommendation systems can be stated. With the help of Data Mining techniques and technologies, enterprises can now forecast future trends and make more educated business decisions.
Data mining is a crucial component of data analytics as a whole and one of the fundamental fields in data science, which makes use of cutting-edge analytics methods to extract valuable information in data sets. It is categorized as a discipline in the field of data science.
HOW DOES DATA MINING WORKS?
Data mining is the process of study and analyzing huge blocks of data to discover significant patterns and trends. It can be further utilized in many different contexts including database marketing, credit risk management, fraud detection, spam email screening, and even to ascertain user emotion.
Many essential characteristics that distinguish Data Mining are discussed below:
DATA MINING PROCESS
Generally, data analysts follow a structure in order to have a better understanding of the Data Mining process. In order to avoid any kind of mishap data mining process is divided into six steps which are:
1. Catching the drift of Business: The first step in any data mining project is to comprehend the core of any organization and the project at hand before touching, extracting, cleaning or analyzing any data. To achieve the objectives at the end, the mining process should be well understood and necessary compliances should be done.
2. Data preprocessing: The second phase involves the selection, cleaning, enrichment, reduction, and transformation of databases. Once the business problem and strategies are resolved, it’s time to process the data. This step also evaluates the restrictions on data, storage, security and collecting and considers how these may affect the data mining procedure.
3. Preparation of data: This method entails statistical analysis of the data followed by the preparation of a graphical visualization of the data in order to obtain estimates of the value. During this stage, data is extracted, transformed, uploaded and calculated and then it is cleaned, standardized, evaluated for errors and reviewed for reasonableness.
4. Model Building: After the data from the previous phase has been acquired, it is time to compute the numbers. Based on the previous data analysis, a suitable model, such as clustering or regression analysis, is chosen. The data can also be fed into predictive models to see how previous data correlates with future outcomes..
5. Evaluating the results: Once the model is ready and all the values of data are uploaded, the results should be properly assessed and verified if the objectives set in the very first stage of the process is fulfilled or not. The outcomes from the analysis be presented to the decision-makers with the aggregated and interpreted results. By determining the findings of the data model, the data-centered aspect of data mining can be concluded.
6. Model update: The last step of the process would be to implement the changes and strategically pivot based on findings. This process can be concluded with management taking necessary steps in accordance to the results of the analysis.
Moreover, it is important to note that Data Mining process models may be different from other models and the steps can be reduced or increased as per the functioning of each model. For example, the Knowledge Discovery Databases model has nine steps, the Cross Industry Standard Process for Data Mining (hereinafter referred to as “CRISP-DM”) model has six steps, and the Sample, Explore, Modify, Model, and Assess (hereinafter referred to as “SEMMA”) process model has five steps.
DATA MINING BENEFITS
The enhanced ability to find hidden patterns, trends, correlations, and anomalies in data sets is what gives Data Mining an advantage and it can be further used to draw business conclusions and strategic planning through conventional data analysis and predictive analytics. Benefits of data mining includes following:
CHALLENGES FACED IN IMPLEMENTING THE DATA MINING PROCESS
To achieve the desired results using Data Mining, data scientists and organizations have to face several challenges which are listed below:
In the event that this is not practicable, they could lessen the impact of incomplete data by highlighting its absence in their reports or by interpreting trends from the outcomes of the available data. To furnish the missing or incomplete data, the process becomes tedious and time-consuming as data analysts have to assess or search for each and every information and compare the same with the data in order to find or complete that sets of data.
The bottom line is modern businesses or agencies have the ability to gather information on customers, products, manufacturing lines, employees among many others but with the help of Data Mining techniques and tools it can be brought together to drive a new value.
Data Mining was introduced with the intention of helping to draw conclusions by evaluating the massive amount of data in order to contribute the improvement and growth of the business. The objective is to find repetitive patterns, trends, or rules that explain the behaviour of the data collected over time. Data collection, analysis, and operational strategy implementation will be the ultimate goal of the Data Mining process.
– Team AMLEGALS assisted by Ms. Juhi Bansal (Intern)
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