What is Data Mining?

What is Data Mining?

Data mining which is also known as Knowledge Discovery in Data (KDD), is a promising tool which helps in discovering hidden valuable knowledge, finding patterns, correlations within large data sets and relationships within your data. Data Science is Multidisciplinary. It is the Umbrella and Data Mining is one of its Components. You’ll be able to recognize and qualify the Patterns with Data Mining, that you were unable to determine earlier. Data mining is a relatively new technology which analyzes large amounts of data and Trends stored in Databases or Data Warehouses, which can’t go beyond simple analysis.

The beauty of Data mining is, it can answer questions that people can’t address just by using query and Reporting Techniques.

This is done using distinct data mining techniques. This could be machine learning, statistical and artificial intelligence (AI).

Data Mining Process

1. Business understanding
   Understanding the Business Objectives and Requirements and then coming up with a Data Mining Plan. This is to achieve both Data Mining and the Business Goals.
2. Data understanding
   Collecting the data from the available sources, doing Data loading and Data integration for Successfully collecting Data. Checking out for the acquired Data if it is complete or not.
3. Data preparation
   It is about identifying the Data, selecting, cleaning and forming it. Data Preparation consumes the whole Data Processing Process.
   Thoughtful data preparation improves the information which was to be discovered through data mining.
4. Model Building
   Collecting Selecting the Modeling Techniques first for Building the Model from the Prepared Dataset and then checking out if the Model meets the Business requirement.

Evaluation

This phase is about evaluating the Business Model. It is about gaining understanding about the Business Processing and testing the Model which was made.

Knowledge Deployment

Knowledge deployment, it is the use of data mining technique within a target environment. Here, you can obtain the insights and actionable information from the data.

Here, the Deployment plan is to made and implemented for Monitoring and maintaining the Future Plans.

Data Mining Process is about Collecting the Data, storing and Managing it. This could be done on the in-house servers or the cloud services. This Data is then Organized, sorted and then presented in an easily accessible Format.

Data Mining Applications:

The application of Data Mining across a variety of industries and disciplines:

1. Communications
   Targeting the most appropriate Campaigns by predicting Customer Behavior. This takes place by taking the Reference of the Customer Data.
   
2. Insurance
   The data mining techniques helps the Companies with the Customer’s history Data and saves them to complex problems concerning fraud and does risk management price.
3. Education
   Data mining helps the educators access the student data, predict their achievement levels data-driven views on student progress. It’s easy to predict the student performance and plan strategies which will keep them focused on their Course.
4. Banking Sector
   Automated algorithms help banks understand their customer behavior. This gives them a better view of market risks, it detects fraud, manages regulatory compliance obligations and helps the Banks Obtain the optimal returns on the marketing investments.
5. Retail
   Large customer databases hold hidden insights, optimizes marketing campaigns to Forecast Sales. This helps to find out which offer works and has a bigger impact on the Customer.

Advantages of Data Mining

1. Campaigning ideas:
   Data mining Process uses the Historical Data for Building Models and Campaigns through Predictive analysis. This helps the Marketers to decide upon the Profitable Product and the Targeted Customers. It even helps the Retail Companies to give a discount on Products that will attract Buyers.
2. Finance / Banking Sector
   Data mining helps to build the Models containing Customers Historic information. This helps the Banks and the Financial Institutions whether to give the Loan or Not.
3. Optimal control parameters:
   Data mining helps to determine the ranges of control parameters. This later helps in Producing More. This is the optimal control parameters which are used to Manufacture as per the quality you desire the most.
4. Government/ Authority:
   Data mining helps government agency and the authorities to dig and analyze the financial transaction and Records. It helps in building patterns that can detect money laundering and keep a check on criminal activities.

Data Mining Techniques

Few of the major Data Mining Techniques are:

1. Association:
   The association technique is based on the relationship between items in the same transaction. It is used in market basket analysis for the identifying products that users are interested in. And the Retailers use the same Technique for finding out the customer’s buying habits
2. Classification:
   Classification method comprises of mathematical techniques such as decision trees, statistics, neural network and linear programming. Classification contains the Software that is Trained to learn how to
3. Clustering:
   Clustering Technique helps in assigning an individual class to the Objects. Putting and arranging the Objects of the same Characteristics in a Group so that they are easily distinguishable.
4. Prediction:
   The prediction technique finds out the connection and Relationship between independent and dependent variables.
5. Sequential Patterns:
   Sequential patterns analysis is one of the data mining technique. It searches similar patterns, hunts for regular events and continuously seeks out for trends in transactional data over a business period.
6. Decision trees:
   The decision tree is one of the most commonly used data mining techniques. This Technique is very Simple. It has got a Question and a Condition which has multiple answers and these Set of Answers are again Connects to a new set of questions.