Data mining is basically a process of understanding and analyzing data of different fields in different perspectives and then summarizing these into a meaningful and useful piece of information. Data mining basically digests available information to generate a report catering to the needs of the individual reading it.
Data mining influences the other business processing areas like statistics, pattern recognition, pattern learning, artificial intelligence and others. That is why it is believed that data mining by itself has no relevance and it cannot be considered a separate field. It is claimed that the term “Data Mining” itself has been forced into business as a separate field, although in reality it is actually a compilation of different fields put together as a single unit. In its real sense, Data Mining is nothing but an area that uses the application of old techniques to deliver something new. To understand how Data Mining fares in comparison to other business process, here are a few summaries.
Data Mining Versus Statistics
Statistics basically uses empirical data in a quantitative method to derive real results that includes numbers and percentages so as to give exact mathematical quantities. Statistics work involves the extraction of quantitative and statistical characteristics from available data. In the field of statistics, the main aim is to get numbers and estimates; here one does not calculate the dependencies at conceptual levels and report an explanation or a qualitative description. There are no explanations for the numbers, one does reason out for the number of estimates extracted. Data Mining not only extracts the numerical estimates, it also provides an explanation for the same. It is far more interactive in comparison to statistics, and besides explanatory theories, there are other processes like data pre-processing and transaction, data reduction, data comprehension, and data cleaning that are involved in data mining. Data mining, therefore, is a totally different process as compared to Statistics.
Data Mining Versus Predictive Analysis
Predictive analysis extracts information from existing data and uses them to predict future trends and behavioral patterns. The aim of predictive analysis is to catch the relationship existing between the variables and then to predict the future results on the basis of the explanatory variables found already existing in the data.. This is exactly what data mining doe. In data mining, after analyzing and summarizing the existing data, the results will be matched to the prediction. From this the term “predictive analysis” is derived, a key concept in data mining.
Data Mining Versus Business Intelligence
Business intelligence, by definition, is a process wherein applications, technologies and concepts are used to gather, store, analyze and provide access to data that would come to the aid of business users or the board of members to make better decisions. Data mining is nowhere close to what business intelligence is. Business intelligence is a concept that is quite broad and deep. Business intelligence also helps to create reports from monthly sales activities, dashboard and scorecards are form a part of business intelligence, therefore, business intelligence in itself is a totally different category.To learn more about business intelligence as a career, click here.
Data Mining Versus Supervised Learning
Supervised learning is a machine learning technique from training data. Supervised learning consists of input objects and desired outputs, wherein the output could be a continuous value or it may be able to predict a class label for the input object. The task of the supervised learner is to predict the value of the function for any of the input objects after having gone through a number of prior inputs and their results. This field also has basically nothing to do with data mining except for getting the numbers or extracting them from existing data. Thus, supervised learning is also a totally different discipline as compared to data mining.
Data mining extracts the details of the required information in the required format. It is more interactive and helps extract the data easily, efficiently and effectively in minimum time, and is an essential ingredient of all business.