Data mining tutorial for beginners pdf

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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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Data Mining Tutorial For Beginners Pdf

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.

Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance. Privacy concerns and ethics[ edit ] While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to peoples' behavior ethical and otherwise. A common way for this to occur is through data aggregation. Data aggregation involves combining data together possibly from various sources in a way that facilitates analysis but that also might make identification of private, individual-level data deducible or otherwise apparent.

Smoothing: It helps to remove noise from the data. Aggregation: Summary or aggregation operations are applied to the data. Generalization: In this step, Low-level data is replaced by higher-level concepts with the help of concept hierarchies.

Data Mining Tutorial: Process, Techniques, Tools & Applications

For example, the city is replaced by the county. Normalization: Normalization performed when the attribute data are scaled up o scaled down. Example: Data should fall in the range Attribute construction: these attributes are constructed and included the given set of attributes helpful for data mining. The result of this process is a final data set that can be used in modeling.

Modelling In this phase, mathematical models are used to determine data patterns. Based on the business objectives, suitable modeling techniques should be selected for the prepared dataset.

Create a scenario to test check the quality and validity of the model. Run the model on the prepared dataset. Results should be assessed by all stakeholders to make sure that model can meet data mining objectives.

Evaluation: In this phase, patterns identified are evaluated against the business objectives. Results generated by the data mining model should be evaluated against the business objectives. Gaining business understanding is an iterative process. In fact, while understanding, new business requirements may be raised because of data mining. A go or no-go decision is taken to move the model in the deployment phase.

Deployment: In the deployment phase, you ship your data mining discoveries to everyday business operations. The knowledge or information discovered during data mining process should be made easy to understand for non-technical stakeholders.

A detailed deployment plan, for shipping, maintenance, and monitoring of data mining discoveries is created. A final project report is created with lessons learned and key experiences during the project. This helps to improve the organization's business policy. Data Mining Techniques 1. Classification: This analysis is used to retrieve important and relevant information about data, and metadata.

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

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. If the data set is not diverse, data mining results may not be accurate.

There are issues like object matching and schema integration which can arise during Data Integration process. It is a quite complex and tricky process as data from various sources unlikely to match easily. Therefore, it is quite difficult to ensure that both of these given objects refer to the same value or not. Here, Metadata should be used to reduce errors in the data integration process. Next, the step is to search for properties of acquired data.

A good way to explore the data is to answer the data mining questions decided in business phase using the query, reporting, and visualization tools. Based on the results of query, the data quality should be ascertained. Missing data if any should be acquired. In this phase, data is made production ready.

The data from different sources should be selected, cleaned, transformed, formatted, anonymized, and constructed if required. Data cleaning is a process to "clean" the data by smoothing noisy data and filling in missing values. For example, for a customer demographics profile, age data is missing.

The data is incomplete and should be filled. In some cases, there could be data outliers. For instance, age has a value Data could be inconsistent. For instance, name of the customer is different in different tables. Data transformation operations change the data to make it useful in data mining. Following transformation can be applied Data transformation: Data transformation operations would contribute toward the success of the mining process. It helps to remove noise from the data. Summary or aggregation operations are applied to the data.

In this step, Low-level data is replaced by higher-level concepts with the help of concept hierarchies. For example, the city is replaced by the county.

Normalization performed when the attribute data are scaled up o scaled down. Data should fall in the range Attribute construction: The result of this process is a final data set that can be used in modeling.

Modelling In this phase, mathematical models are used to determine data patterns.

Data Mining Tutorial

Based on the business objectives, suitable modeling techniques should be selected for the prepared dataset. Create a scenario to test check the quality and validity of the model.

Run the model on the prepared dataset. Results should be assessed by all stakeholders to make sure that model can meet data mining objectives. In this phase, patterns identified are evaluated against the business objectives.

Results generated by the data mining model should be evaluated against the business objectives. Gaining business understanding is an iterative process. In fact, while understanding, new business requirements may be raised because of data mining.

[PDF] Data Mining; A Conceptual Overview - Semantic Scholar

A go or no-go decision is taken to move the model in the deployment phase. In the deployment phase, you ship your data mining discoveries to everyday business operations.

The knowledge or information discovered during data mining process should be made easy to understand for non-technical stakeholders. A detailed deployment plan, for shipping, maintenance, and monitoring of data mining discoveries is created. A final project report is created with lessons learned and key experiences during the project. This helps to improve the organization's business policy. Data Mining Techniques 1. This analysis is used to retrieve important and relevant information about data, and metadata.

This data mining method helps to classify data in different classes. 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 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 has used a combination of the other data mining techniques like trends, sequential patterns, clustering, classification, etc.

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. 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. 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. R language is an open source tool for statistical computing and graphics. 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.

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