This tutorial has been prepared for computer science graduates to help them understand the basic-to-advanced concepts related to data mining. Prerequisites. Data Mining Tutorial in PDF - Learn Data Mining in simple and easy steps starting from basic to advanced concepts with examples Overview, Tasks, Data Mining. PDF | On Jan 1, , Graham Williams and others published A Data Mining Tutorial. –Brings together expertise in Machine Learning, Statistics,. Numerical .
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And Where Has it Come From? Parallel. Algorithms. Machine. Learning. High. Performance. Computers. Database. Visualisation. Data Mining. Data Mining is defined as extracting the information from the huge set of data. tutorial you should have a understanding of basic database concepts such as. Overview of data mining. Emphasis is placed on basic data mining concepts. Techniques for uncovering interesting data patterns hidden in large data sets.
For high ROI on his sales and marketing efforts customer profiling is important. He has a vast data pool of customer information like age, gender, income, credit history, etc.
But its impossible to determine characteristics of people who prefer long distance calls with manual analysis. Using data mining techniques, he may uncover patterns between high long distance call users and their characteristics.
Marketing efforts can be targeted to such demographic.
Example 2: A bank wants to search new ways to increase revenues from its credit card operations. They want to check whether usage would double if fees were halved. Bank has multiple years of record on average credit card balances, payment amounts, credit limit usage, and other key parameters. They create a model to check the impact of the proposed new business policy.
R language is an open source tool for statistical computing and graphics.
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R has a wide variety of statistical, classical statistical tests, time-series analysis, classification and graphical techniques. It offers effective data handing and storage facility. Learn more here Oracle Data Mining: This Data mining tool allows data analysts to generate detailed insights and makes predictions.
It helps predict customer behavior, develops customer profiles, identifies cross-selling opportunities. Learn more here Benefits of Data Mining: Data mining technique helps companies to get knowledge-based information. Data mining helps organizations to make the profitable adjustments in operation and production. The data mining is a cost-effective and efficient solution compared to other statistical data applications.
Data mining helps with the decision-making process. Facilitates automated prediction of trends and behaviors as well as automated discovery of hidden patterns.
It can be implemented in new systems as well as existing platforms It is the speedy process which makes it easy for the users to analyze huge amount of data in less time. Disadvantages of Data Mining There are chances of companies may sell useful information of their customers to other companies for money.
For example, American Express has sold credit card downloads of their customers to the other companies. Many data mining analytics software is difficult to operate and requires advance training to work on. Different data mining tools work in different manners due to different algorithms employed in their design.
Data Mining Tutorial
Therefore, the selection of correct data mining tool is a very difficult task. The data mining techniques are not accurate, and so it can cause serious consequences in certain conditions.
Data Mining Applications Applications Usage Communications Data mining techniques are used in communication sector to predict customer behavior to offer highly targetted and relevant campaigns. Insurance Data mining helps insurance companies to price their products profitable and promote new offers to their new or existing customers.
Education Data mining benefits educators to access student data, predict achievement levels and find students or groups of students which need extra attention. For example, students who are weak in maths subject. Manufacturing With the help of Data Mining Manufacturers can predict wear and tear of production assets.
They can anticipate maintenance which helps them reduce them to minimize downtime. Banking Data mining helps finance sector to get a view of market risks and manage regulatory compliance. It helps banks to identify probable defaulters to decide whether to issue credit cards, loans, etc. Retail Data Mining techniques help retail malls and grocery stores identify and arrange most sellable items in the most attentive positions.
It helps store owners to comes up with the offer which encourages customers to increase their spending.
Service Providers Service providers like mobile phone and utility industries use Data Mining to predict the reasons when a customer leaves their company. They analyze billing details, customer service interactions, complaints made to the company to assign each customer a probability score and offers incentives. E-Commerce E-commerce websites use Data Mining to offer cross-sells and up-sells through their websites.
One of the most famous names is site, who use Data mining techniques to get more customers into their eCommerce store. Super Markets Data Mining allows supermarket's develope rules to predict if their shoppers were likely to be expecting. By evaluating their downloading pattern, they could find woman customers who are most likely pregnant.
They can start targeting products like baby powder, baby shop, diapers and so on. Crime Investigation Data Mining helps crime investigation agencies to deploy police workforce where is a crime most likely to happen and when? Bioinformatics Data Mining helps to mine biological data from massive datasets gathered in biology and medicine.
Data Mining is all about explaining the past and predicting the future for analysis. Data mining helps to extract information from huge sets of data. It is the procedure of mining knowledge from data. Important Data mining techniques are Classification, clustering, Regression, Association rules, Outer detection, Sequential Patterns, and prediction R-language and Oracle Data mining are prominent data mining tools. The main drawback of data mining is that many analytics software is difficult to operate and requires advance training to work on.
What is Data Warehousing? A data warehousing is a technique for collecting and managing data from What is Keras?
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This data mining method helps to classify data in different classes. Clustering: Clustering analysis is a data mining technique to identify data that are like each other. This process helps to understand the differences and similarities between the data.
Regression: Regression analysis is the data mining method of identifying and analyzing the relationship between variables.
It is used to identify the likelihood of a specific variable, given the presence of other variables. Association Rules: This data mining technique helps to find the association between two or more Items.
It discovers a hidden pattern in the data set. Outer detection: This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. Outer detection is also called Outlier Analysis or Outlier mining. Sequential Patterns: This data mining technique helps to discover or identify similar patterns or trends in transaction data for certain period.
Prediction: Prediction has used a combination of the other data mining techniques like trends, sequential patterns, clustering, classification, etc.
Data Mining Tutorial in PDF
It analyzes past events or instances in a right sequence for predicting a future event. Challenges of Implementation of Data mine: Skilled Experts are needed to formulate the data mining queries. Overfitting: Due to small size training database, a model may not fit future states.
Data mining needs large databases which sometimes are difficult to manage Business practices may need to be modified to determine to use the information uncovered.
Data Mining Tutorial in PDF
If the data set is not diverse, data mining results may not be accurate. Integration information needed from heterogeneous databases and global information systems could be complex Data mining Examples: Example 1: Consider a marketing head of telecom service provides who wants to increase revenues of long distance services.
For high ROI on his sales and marketing efforts customer profiling is important.
He has a vast data pool of customer information like age, gender, income, credit history, etc. But its impossible to determine characteristics of people who prefer long distance calls with manual analysis. Using data mining techniques, he may uncover patterns between high long distance call users and their characteristics. Marketing efforts can be targeted to such demographic. Example 2: A bank wants to search new ways to increase revenues from its credit card operations. They want to check whether usage would double if fees were halved.
Bank has multiple years of record on average credit card balances, payment amounts, credit limit usage, and other key parameters. They create a model to check the impact of the proposed new business policy. Data Mining Tools Following are 2 popular Data Mining Tools widely used in Industry R-language: R language is an open source tool for statistical computing and graphics.Facilitates automated prediction of trends and behaviors as well as automated discovery of hidden patterns.
The data is incomplete and should be filled. Username Password. Data mining helps insurance companies to price their products profitable and promote new offers to their new or existing customers. This tutorial explains about overview and the terminologies related to the data mining and topics such as knowledge discovery, query language, classification and prediction, decision tree induction, cluster analysis, and how to mine the Web.
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