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Data Mining is a process of finding potentially useful patterns from huge data sets. It is a multi-disciplinary skill that uses machine learning, statistics, and AI to extract information to evaluate future events probability. The insights derived from Data Mining are used for marketing, fraud detection, scientific discovery, etc. Data Mining is all about discovering hidden, unsuspected, and previously unknown yet valid relationships amongst the data. Data mining is also called Knowledge Discovery in Data (KDD), Knowledge extraction, data/pattern analysis, information harvesting, etc. In this Data Mining tutorial, you will learn the fundamentals of Data Mining like- Types of DataData mining can be performed on following types of data
Implementation Process of Data MiningData Mining Implementation ProcessLet’s study the Data Mining implementation process in detail Business understanding:In this phase, business and data-mining goals are established.
Data understanding:In this phase, sanity check on data is performed to check whether its appropriate for the data mining goals.
Data preparation:In this phase, data is made production ready. The data preparation process consumes about 90% of the time of the project. 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 300. 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. Smoothing: It helps to remove noise from the data. Aggregation: Summary or aggregation operations are applied to the data. I.e., the weekly sales data is aggregated to calculate the monthly and yearly total. Generalization: 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: Normalization performed when the attribute data are scaled up o scaled down. Example: Data should fall in the range -2.0 to 2.0 post-normalization. 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. ModellingIn this phase, mathematical models are used to determine data patterns.
Evaluation:In this phase, patterns identified are evaluated against the business objectives.
Deployment:In the deployment phase, you ship your data mining discoveries to everyday business operations.
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. 2. 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. 3. 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. 4. 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. 5. 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. 6. Sequential Patterns:This data mining technique helps to discover or identify similar patterns or trends in transaction data for certain period. 7. Prediction:Prediction has used a combination of the other techniques of data mining 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:
Data mining Examples:Now in this Data Mining course, let’s learn about Data mining with 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. For example, he might learn that his best customers are married females between the age of 45 and 54 who make more than $80,000 per year. 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. The data results show that cutting fees in half for a targetted customer base could increase revenues by $10 million. Data Mining ToolsFollowing are 2 popular Data Mining Tools widely used in Industry R-language: 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: Oracle Data Mining popularly knowns as ODM is a module of the Oracle Advanced Analytics Database. 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:
Disadvantages of Data Mining
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