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What is Data Mining? Know it's process, application, technique, advantages

What is Data Mining?

Data Minning Process, Technique, Evaluation

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).

The important properties of data mining are:

It discovers patterns Automatically

It Predicts the likely outcomes

It Creates actionable information

It Focuses on the large data sets and databases

Data Minning Process, Technique, Evaluation

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.

Data Minning Tools

Knime

KNIME Analytics Platform offers a Perfect Toolbox to the Data Scientists. It helps you in discovering the hidden potential in your Data.

RapidMiner

RapidMiner helps with statistical modeling, evaluation, data visualization, data mining and machine learning procedures. It also helps with Business and Predictive Analysis.

OpenRefine

Got Messy Data? Open Refine will help you to clean, analyze, transform and shape the Data for Predictive Modeling.

NodeXL

NodeXL is a data visualization Tool. It is a Data analysis software which comprises of relationships and networks. NodeXL keeps an Exact count and Check on Data, the Metrics, access to social media network data importers, and automation.

Google Fusion Tables

Google Fusion Tables is another interesting Data Analysis Tool. It is often Considered a Competitive and Advanced version of Google Spreadsheets. It helps in Mapping, analyzing and Visualizing Data. It can Filter and Visualize 1000+ rows.  Analysts use it to delete, insert, update and query data programmatically.

QlikView

QlikView is one of the most outstanding tools of BI industry around the world. It is not a Statistical Software but It helps in Deriving business insights by building your own rich Analytics Applications and presenting it in an amazing manner. It has got art visualization capabilities, and the Potential to control the Data.

Solver

The Solver is an Add-in of Microsoft Office. It is an Excel add-in program which you can avail only if you’ve got Microsoft Excel or MS Office.

Wolfram Alpha

Wolfram Alpha, Stephen Wolfram. It is an Advanced Computational Knowledge or Answering Engine which helps you obtain the answers of the Exact Queries. Wolfram directly responses to technical searches and solves calculus problems. You can make informative charts, Topic Overviews and Graphs and can reach high-Pricing History.

Tableau Public

Tableau, one of the best and Trending Data Analytics tools, a simple and intuitive tool which. With Tableau Business can publish interactive data visualizations to the web for absolutely no cost.

Google Search Operators

Google is an undeniably powerful resource which helps to Filter Google Results and the search operators have already taken it a step high. With Google Search Operators you get Google Results which is instant and very Useful. It is called Google’s most powerful data analysis tool which helps to discover new information or market research.

Conclusion:

Data Mining brings a Lot of Benefits to the Businesses. It helps the individuals, the Society as well as the governments.

It holds great potential to identify best practices that improve care and reduce costs.

Researchers take the help of data mining approaches like Data Mining and Machine Learning, multi-dimensional databases, soft computing, data visualization and statistics.