Data Miningis a process of examining patterns of large sets of data by using various techniques like machine learning, statistics, and database systems to extract meaningful outcomes. By inspecting and collecting data, patterns are discovered through Data Mining.
In simple words, raw data is collected by companies and by data mining, raw data is turned into useful information. There are several types of Data Mining:
· Pictorial Data Mining
· Text Mining
· Social Media Mining
· Web Mining
· Audio Mining
· Video Mining
Data mining process: Data Mining is a process to identify patterns from a large amount of data. Following are steps involved in Data Mining
1. Data Cleaning: In this step, data is cleaned, incomplete data is removed as it can lead to poor insights or failure. So, data is cleaned as with industry standards.
2. Data Integration: In this, data miners combine different data sets to perform analysis. This eliminates any inconsistent information.
3. Data Reduction: Data reduction refers to extracting relevant information for data analysis and pattern evaluation. Engineers reduce the data and relevant data is left for analytics purposes.
4. Data Transformation: In this, data is transformed into an acceptable form to align with mining goals. It encompasses data mapping, eliminating noise from data, normalization etc.
5. Data Mining: Data Mining is done by all organizations to extract useful information and patterns to generate solutions to any problem. Specialists use clustering, classification etc. for data mining steps.
6. Pattern Evaluation: In this, a pattern is studied that can generate business knowledge. The team summarizes information to make it easier to understand.
7. Representing Knowledge: At last, data analysts share information with others by using various techniques like reports, mining tools etc. Data is represented to owners and other parties in the final product which can be understood by them easily.
Techniques of Data Mining:
Several techniques are used in Data Mining to extract useful results from raw data. These are:
· Classification: In Classification, items are classified in a data set into different classes. It classifies items in a data set into predefined groups. It uses linear programming, statistics, decision trees etc.
· Clustering: The clustering technique determines object groupings such that objects or items of the same cluster form one group or items of similar nature form one group. Clustering is used in market segmentation, for example, in libraries where books of the same subjects are kept on one shelf so that readers don’t face difficulty in finding particular subject books.
· Prediction: Prediction techniques are also called regression techniques. In this, prediction power is used to predict the relationship between independent and dependent variables. These techniques are very useful in data science and are the simplest technique.
· Association Rule Discovery: It is the most used data mining technique in which transactions and relationships between all its items are used to identify patterns. This technique is very useful to study consumer behaviour.
Application of Data Mining: Although data mining is used in all sectors here are a few of them listed below:
1. Telecom Industry: Telecom industry is growing at a fast pace, data mining can help the industry to improve quality service. Techniques can be used to analyse fraudulent users, pattern analysis for spatiotemporal data etc.
2. Retail Industry: The retail sector holds a major position in the market and requires data related to sales, purchases, delivery of goods, customer service etc. Data mining can be used to analyse buying patterns, improve customer service etc.
3. Education Industry: Education is one of the most important and high demand sectors that are looking for unique solutions to fulfil today’s needs. Data mining can be used to examine students’ behaviour and predict which students will enrol for which program etc.
4. Criminal Investigation: Data mining can be used for studying crimes characteristics, which will help in making easy procedures for criminal investigation.
5. Financial Sector: The banking and financial sector are the backbone of any economy. Data mining is used in these sectors to determine credit ratings, predict loan payments, investment patterns etc. Data mining can make these tasks more manageable.
6. Counter-Terrorism: Data mining can be used to help the defence sector and police administration tasks also, to counter-terrorism like where to deploy the workforce etc.
Other sectors like Biological data analysis, spatial data mining, energy industry, manufacturing unit, farming, science and engineering etc. where data mining can convert administration tasks manageable. Data mining has become an important part of all sectors and organizations.
Why Has Data Mining Become Important?
As data has become an expensive asset for all organizations to be ahead of their competitors. Data is increasing day by day which has made it difficult to manage. So, here comes data mining which converts this data into meaningful information. By applying patterns and classification, useful insight can be extracted from it and essential decisions can be made out of it. This is the reason why data mining has become important and popular over time.
Lastly, data mining is an important component for all organizations and if you want to learn how user information is extracted from raw data using various techniques,data science is a field to study. Data Mining is an important element of data science.