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This book presents 15 real-world applications on data mining with R, selected Links to the PDF file of the report were also circulated in five. The inclusion of concrete examples and applications is highly encouraged. The scope of Data mining with R: learning with case studies / Luís Torgo. p. cm. data mining applications with r data mining applications with pdf. Data mining is the process of discovering patterns in large data sets involving methods at the.
Personal information is secured with SSL technology. Free Shipping No minimum order. Description R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.
Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages.
Extending R is also eased by its lexical scoping rules. Dynamic and interactive graphics are available through additional packages. The prefix  indicates that the list of elements following it on the same line starts with the first element of the vector a feature that is useful when the output extends over multiple lines.
R's data structures include vectors , matrices , arrays, data frames similar to tables in a relational database and lists. The scalar data type was never a data structure of R. R uses S-expressions to represent both data and code. Functions are first-class and can be manipulated in the same way as data objects, facilitating meta-programming , and allow multiple dispatch. Variables in R are lexically scoped and dynamically typed.
Function arguments are passed by value, and are lazy -- that is to say, they are only evaluated when they are used, not when the function is called. R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions.
A generic function acts differently depending on the classes of arguments passed to it. In other words, the generic function dispatches the function method specific to that class of object.
For example, R has a generic print function that can print almost every class of object in R with a simple print objectname syntax. R has also been identified by the FDA as suitable for interpreting data from clinical research.
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. 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. 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.