Data mining
- 1. Seminar Lab Hardavi Shah ( 17012011049 ) 5th CE –A A3 Batch Data Mining
- 2. Overview History of Data Mining Definition of Data Mining What is Data Mining? Data Mining as a whole Process Why Data Mining is required Applications of Data Mining Functions for Data Mining
- 3. History of Data Mining The term "Data mining" was introduced in the 1990s, but data mining is the evolution of a field with a long history. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s).
- 4. Definition of Data Mining Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.
- 6. What is Data Mining Data mining is the analysis step of the "knowledge discovery in databases" process. Aside from the raw analysis step, it also involves database and data management aspects, data pre- processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
- 7. What is Data Mining Technically, data mining is the computational process of analyzing data from different perspective, dimensions, angles and categorizing/summarizing it into meaningful information. Data Mining can be applied to any type of data e.g. Data Warehouses, Transactional Databases, Relational Databases, Multimedia Databases, Spatial Databases, Time-series Databases, World Wide Web.
- 8. Data Analysis Vs. Data Mining The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset. e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine- learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.
- 9. Data Mining as a whole process The whole process of Data Mining comprises of three main phases: 1. Data Pre-processing – Data cleaning , integration , selection and transformation takes place 2. Data Extraction – Occurrence of exact data mining 3. Data Evaluation and Presentation – Analyzing and presenting results
- 11. Why Data Mining is required?? There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it. Extraction of information is not the only process we need to perform. Data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Pattern Evaluation and Data Presentation.
- 12. Data mining applications The information or knowledge extracted so can be used for any of the following applications − o Market Analysis o Fraud Detection o Customer Retention o Production Control o Science Exploration Apart from these, data mining can also be used in the areas of sports , astrology , and Internet Web Surf-Aid.
- 14. Market Analysis and Management Market Analysis is a technique which gives the careful study of purchases done by a customer in a super market. The concept is basically applied to identify the items that are bought together by a customer. Say, if a person buys bread, what are the chances that he/she will also purchase butter. This analysis helps in promoting offers and deals by the companies. The same is done with the help of data mining.
- 15. Market Analysis and Management Listed below are the various fields of market where data mining is used − Customer Profiling − Data mining helps determine what kind of people buy what kind of products. Identifying Customer Requirements − Data mining helps in identifying the best products for different customers. It uses prediction to find the factors that may attract new customers.
- 16. Market Analysis and Management Cross Market Analysis − Data mining performs Association/correlations between product sales. Target Marketing − Data mining helps to find clusters of model customers who share the same characteristics such as interests, spending habits, income, etc. Determining Customer purchasing pattern − Data mining helps in determining customer purchasing pattern.
- 17. Market Analysis and Management Providing Summary Information − Data mining provides us various multidimensional summary reports.
- 18. Corporate Analysis and Risk Management Data mining is used in the following fields of the Corporate Sector − Finance Planning and Asset Evaluation − It involves cash flow analysis and prediction, contingent claim analysis to evaluate assets. Resource Planning − It involves summarizing and comparing the resources and spending. Competition − It involves monitoring competitors and market directions.
- 19. Fraud Detection Data mining is also used in the fields of credit card services and telecommunication to detect frauds. In fraud telephone calls, it helps to find the destination of the call, duration of the call, time of the day or week, etc. It also analyzes the patterns that deviate/differs from expected norms (normal condition).
- 20. Functions for Data Mining Data mining deals with the kind of patterns that can be mined. On the basis of the kind of data to be mined, there are two categories of functions involved in Data Mining − Descriptive Classification and Prediction
- 22. Descriptive Function The descriptive function deals with the general properties of data in the database. Here is the list of descriptive functions− Class/Concept Description Mining of Frequent Patterns Mining of Associations Mining of Correlations Mining of Clusters
- 23. Classification and Prediction Classification is the process of finding a model that describes the data classes or concepts. The purpose is to be able to use this model to predict the class of objects whose class label is unknown. This derived model is based on the analysis of sets of training data.
- 24. Classification and Prediction The derived model can be presented in the following forms− Classification (IF-THEN) Rules Decision Trees Mathematical Formulae Neural Networks
- 25. Classification and Prediction The list of functions involved in these processes are as follows − Classification Prediction Outlier Analysis Evolution Analysis
- 26. Thank You