Data mining concepts
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Data mining involves discovering patterns and trends in large data sets. It uses techniques from statistics, mathematics, and computer science to find hidden patterns and relationships in the data. Data mining has applications in marketing, finance, manufacturing, and healthcare to gain insights from data. The data mining process involves defining the problem, preparing data, exploring and analyzing the data, building models, validating models, and deploying the best models. Issues in data mining include handling different data types, incorporating background knowledge, and protecting privacy and security. Active areas of research will continue advancing data mining techniques.
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Data Mining:-
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information.
Data Mining is the Process that is used by big companies or organizations to handle,balance and analyzing big data.
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factor.
It is used by the companies to increase their revenue or cut their costs or both.
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SOFTWARES FOR DATA MINING:
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Mircrsoft SQL SERVER 2008.
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One Super Market in Canada used data mining capacity of Oracle Software to analyze local buying patterns.They discovered that when Mens bought Food for home on Saturday and Sunday They like to tended to buy beer.On Other days of the week mens don’t usually buy beers.
The Shopkeeper said to his workers to put sufficient amount of beers on Saturday and Sunday.In this way income of the shop was increased.
BI refers to applications & technologies which are use to gather information about their company opertaions.
Data Mining is importand part of business intellegence.
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In Finance Data Mining is used for Credit Cards Analysis.
Astronomy:
Palomar Obstervatory discovered 22 quasars with the help of Data Mining.
3) Telecommunication:
In Telecommunication Data Mining is used for Call Records.
4) Offices:
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Assoication Rule is used for store layout. Etc.
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THANK YOU...!!!
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The document provides an overview of data, information, knowledge, and data mining. It defines data as facts/observations/measurements, information as processed data that is useful (e.g. for decision making), and knowledge as patterns in data/information with a high degree of certainty. Data mining is described as the process of extracting useful but non-obvious information from large databases through an interactive and iterative process. Common business applications and technologies involved in data mining are also discussed.
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- Common data mining tasks include classification, regression, clustering, association analysis, and anomaly detection. These can be used to solve problems like product recommendations, student enrollment predictions, and fraud detection.
- The key steps in data mining typically involve data preparation, exploration, model development, and result interpretation. Association rule mining is commonly used and aims to find relationships between variables in large datasets.
Case study for DWDM
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2) Key steps in the process included data preprocessing, statistical analysis, feature selection, modeling with classifiers like boosted Bayesian networks, and using the model to predict likely attriters to target retention efforts.
3) Field testing found the top of the attrition list identified by the ensemble model did contain concentrated attriters, and the data mining approach was effective for the bank's retention purposes.
Data Mining
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Data mining
This document provides an introduction to data mining. It defines data mining as the process of analyzing large amounts of data from different perspectives to discover meaningful patterns and extract useful information. It discusses why data mining is needed due to issues like data explosion. It also outlines some common data mining techniques like classification, clustering, and association analysis. Additionally, it provides examples of applications of data mining in various industries and discusses advantages like improved marketing and benefits to governments while also acknowledging disadvantages around privacy, security, and potential misuse of information.
Application of KDD & its future scope
Data mining refers to extracting hidden patterns from large databases and is a step in the Knowledge Discovery in Databases (KDD) process. KDD is the broader process of finding knowledge within data and involves data preparation, pattern analysis, and knowledge evaluation. It is needed due to the impracticality of manually analyzing large, complex databases. The KDD process includes understanding goals, data selection, preprocessing, mining, pattern recognition, interpretation, and discovery. Examples of applying KDD include grouping students, predicting enrollments, and assessing student performance.
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The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then discusses why data mining is needed due to the explosive growth of data from various sources. Data mining is defined as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. The knowledge discovery process involves identifying a problem, mining data to transform it into actionable information, acting on the information, and measuring the results. The document outlines different types of data that can be mined, including structured, transactional, time-series, spatial, multimedia, and web data. Common data mining tasks are also described such as classification, prediction, clustering,
Key Principles Of Data Mining
Data mining involves finding patterns in large datasets that can help improve business outcomes. It uses computer processes and statistical techniques to analyze datasets too large for manual review. The CRISP process standardizes the data mining workflow into six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Key modeling techniques include supervised learning methods like decision trees and regression as well as unsupervised clustering. Ongoing evaluation and refinement of models is important as data and business needs change over time.
Data Mining Techniques
The document discusses data mining and its processes. It states that data mining involves extracting useful information and patterns from large amounts of data through processes like data cleaning, integration, transformation, mining, and presentation. This extracted knowledge can then be applied to various domains such as fraud detection, market analysis, and science exploration.
Data Mining: What is Data Mining?
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
Data Mining and Data Warehouse
introduction, data mining, why data mining, application of data mining, steps of data mining, threat of data mining, solution of data mining, role of data mining, data warehouse, oltp & olap, data warehouse, data mining tools, latest research
Data Mining: Future Trends and Applications
Abstract: Knowledge has played a significant role on human activities since his development. Data mining is the process of
knowledge discovery where knowledge is gained by analyzing the data store in very large repositories, which are analyzed
from various perspectives and the result is summarized it into useful information. Due to the importance of extracting
knowledge/information from the large data repositories, data mining has become a very important and guaranteed branch of
engineering affecting human life in various spheres directly or indirectly. The purpose of this paper is to survey many of the
future trends in the field of data mining, with a focus on those which are thought to have the most promise and applicability
to future data mining applications.
Keywords: Current and Future of Data Mining, Data Mining, Data Mining Trends, Data mining Applications.
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The document discusses data warehousing, knowledge discovery in databases (KDD), and data mining. It defines a data warehouse as a subject-oriented collection of integrated and non-volatile data used to support management decision making. Data mining is extracting knowledge from large amounts of data and has applications in business transactions, ecommerce, healthcare, and more. Specifically for banking, data mining can be used for marketing, risk management, and customer acquisition/retention by identifying patterns in large customer data sets.
Data mining
my topic is data mining technique.. what is data mining,and its type,application are included in this presentation..
Data mining
Data mining involves extracting useful patterns and knowledge from large amounts of data. It is the process of discovering hidden patterns in large datasets. Key techniques of data mining include classification, clustering, association rule learning, and prediction. Data mining has various applications such as customer relationship management, fraud detection, market basket analysis, education, manufacturing, and healthcare. Knowledge discovery is the overall process of discovering useful knowledge from data, where data mining is one important step that analyzes and extracts patterns from data.
Application areas of data mining
This presentation includes major application areas of data mining and its techniques in real world.This ppt includes various field where data mining is playing a crucial role in the development of every sector by its techniques.i hope it would be helpful to everyone.
Group7_Datamining_Project_Report_Final
This document describes a data mining project to analyze bank marketing data using classification algorithms. The goal is to predict whether a client will subscribe to a term deposit based on their attributes. The dataset contains information on over 61,000 customers. Pre-processing steps like data cleaning, handling missing values, removing outliers, and scaling are performed. Algorithms like Naive Bayes, J48 decision trees, and PART are applied and their results compared to determine the best predictor of subscriptions.
Data Mining
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This document discusses data mining, including its components of knowledge discovery and prediction. It defines data mining as applying computer methods to infer new information from existing data. The document outlines different types of data mining like data dredging and relational vs. propositional data. It provides examples of how data mining is used in business, science, health, and other domains. Privacy concerns are raised, and controversies like Facebook's Beacon program are discussed.Data Mining
This Is just a little overview on it not fully explaned.
Data Mining:-
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information.
Data Mining is the Process that is used by big companies or organizations to handle,balance and analyzing big data.
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factor.
It is used by the companies to increase their revenue or cut their costs or both.
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two.
Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries.
SOFTWARES FOR DATA MINING:
Microsoft SQL SERVER 2005.
Mircrsoft SQL SERVER 2008.
Oracle Data Mining etc.
One Super Market in Canada used data mining capacity of Oracle Software to analyze local buying patterns.They discovered that when Mens bought Food for home on Saturday and Sunday They like to tended to buy beer.On Other days of the week mens don’t usually buy beers.
The Shopkeeper said to his workers to put sufficient amount of beers on Saturday and Sunday.In this way income of the shop was increased.
BI refers to applications & technologies which are use to gather information about their company opertaions.
Data Mining is importand part of business intellegence.
Some Basic Examples of Use of Data Mining Are Given Below:
In Finance Data Mining is used for Credit Cards Analysis.
Astronomy:
Palomar Obstervatory discovered 22 quasars with the help of Data Mining.
3) Telecommunication:
In Telecommunication Data Mining is used for Call Records.
4) Offices:
In Offices it is used for to balance data and records of the staff. etc
Following are some of the types if Data Mining:
Assoication Rule is used for store layout. Etc.
Classification is used for weather prediction. Etc.
Clustering is used for Graphical Represention of Universe.
Sequential Pattern is used for medical diagnosis.
THANK YOU...!!!
Data Mining in telecommunication industry
Telecommunication companies generate huge volumes of data from their operational systems. They use data mining methods and business intelligence technology to handle business problems by analyzing call detail, customer, and network data. The main applications of data mining in telecommunications include fraud detection, network fault isolation, and improving market effectiveness. Data mining helps telecom companies detect fraud, gain customer insights, retain customers, determine profitable products and services, and identify factors influencing customer call patterns.
Introduction to Data Mining
The document provides an overview of data, information, knowledge, and data mining. It defines data as facts/observations/measurements, information as processed data that is useful (e.g. for decision making), and knowledge as patterns in data/information with a high degree of certainty. Data mining is described as the process of extracting useful but non-obvious information from large databases through an interactive and iterative process. Common business applications and technologies involved in data mining are also discussed.
Data mining
- Data mining involves discovering novel patterns from large databases using algorithms and computers. It aims to find hidden patterns in datasets by analyzing attribute correlations.
- Common data mining tasks include classification, regression, clustering, association analysis, and anomaly detection. These can be used to solve problems like product recommendations, student enrollment predictions, and fraud detection.
- The key steps in data mining typically involve data preparation, exploration, model development, and result interpretation. Association rule mining is commonly used and aims to find relationships between variables in large datasets.
Case study for DWDM
1) The document discusses using data mining techniques to analyze customer attrition for a retail bank. It outlines the process used, including problem definition, data selection, modeling with techniques like decision trees and neural networks, and field testing results.
2) Key steps in the process included data preprocessing, statistical analysis, feature selection, modeling with classifiers like boosted Bayesian networks, and using the model to predict likely attriters to target retention efforts.
3) Field testing found the top of the attrition list identified by the ensemble model did contain concentrated attriters, and the data mining approach was effective for the bank's retention purposes.
Data Mining
Data mining is the process of discovering useful patterns from large amounts of data using statistical, mathematical, and artificial intelligence techniques. It involves applying these techniques to extract and identify useful information from large datasets. Data mining draws from multiple disciplines including statistics, pattern recognition, mathematical modeling, information systems, and machine learning. It has various applications in domains such as customer relationship management, banking, retailing, manufacturing, insurance, software, government, travel, and healthcare. The CRISP-DM process provides a standard methodology for data mining projects involving six steps: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
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This document provides an overview of artificial neural networks and their application in data mining techniques. It discusses neural networks as a tool that can be used for data mining, though some practitioners are wary of them due to their opaque nature. The document also outlines the data mining process and some common data mining techniques like classification, clustering, regression, and association rule mining. It notes that neural networks, as a predictive modeling technique, can be useful for problems like classification and prediction.
Introduction To Data Mining
This document provides an overview of data mining and knowledge discovery in databases. It discusses why data mining is needed due to large volumes of data, describes the data mining process including data preparation, transformation, mining methods and model evaluation. Specific data mining techniques discussed include association rule mining to find frequent patterns in transactional data and decision tree learning as a supervised learning method to classify instances.
Introduction To Data Mining
The document introduces data mining and knowledge discovery in databases. It discusses why data mining is needed due to large datasets that cannot be analyzed manually. It also covers the data mining process, common data mining techniques like association rules and decision trees, applications of data mining in various domains, and some popular data mining tools.
3 marketing research
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Data mining (prefinals)
Data mining involves extracting useful information from large datasets. It begins by analyzing simple data to develop representations, then extends this to more complex datasets. Data mining has applications in retail, banking, insurance, and medicine. The main data mining operations are predictive modeling, database segmentation, link analysis, and deviation detection. The CRISP-DM process standardizes the data mining process into business understanding, data understanding, data preparation, modeling, evaluation, and deployment phases.
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Data mining
This document provides an overview of data mining. It introduces data mining and its goals, which include prediction, identification, classification, and optimization. The typical architecture of a data mining system is explained, including its major components. Common data mining techniques like classification, clustering, and association are also outlined. Examples are provided to illustrate techniques. The document concludes by discussing advantages and uses of data mining along with some popular data mining tools.
17 cs002
This course covers data warehousing, data mining, and knowledge discovery techniques. Students will learn how to design data warehouse schemas, apply data mining techniques like clustering, classification, and association analysis to real-world datasets. They will discover patterns and rules from large volumes of data. The course explores technologies used to support business intelligence and decision making. Students will gain skills in implementing classification and clustering algorithms, extracting hidden rules, and extracting knowledge from text databases.
2011 Shopper Insights Brochure
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Data Mining and Knowledge Discovery in Large Databases
This document outlines topics in data mining and knowledge discovery in large databases. It discusses hierarchical, divisive, and density-based clustering techniques. It also mentions k-nearest neighbors and support vector machines classification algorithms. Additionally, it notes DBSCAN and density/connectivity for spatial data mining and regulatory networks for eco-finance and gene-environment applications. The document provides an introduction to data mining concepts and examples of analyzing retail, banking, and telecommunications customer data.
Data Mining Applications And Feature Scope Survey
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
Data Mining – A Perspective Approach
This document discusses data mining and provides an overview of the topic. It begins by defining data mining as the process of analyzing large amounts of data to discover hidden patterns and rules. The goal is to analyze this data and summarize it into useful information that can be used to make decisions.
It then describes some common data mining techniques like decision trees, neural networks, and clustering. It also discusses the typical stages of a data mining project, including business understanding, data preparation, modeling, evaluation, and deployment.
Finally, it provides examples of applications for data mining, such as in healthcare to identify patterns in patient data, education to improve learning outcomes, and manufacturing to enhance product quality. In summary, the document outlines the
Ch35
The document discusses data mining, including its definition, main operations, techniques, applications, and relationship to data warehousing. The four main data mining operations are predictive modeling, database segmentation, link analysis, and deviation detection. Each operation uses specific techniques like classification, clustering, association rule mining, and visualization. Data mining is applied in domains like retail, banking, insurance, and medicine. It works best with large, clean datasets typically stored in data warehouses.
Today's BI and Data Mining ecosystem
Visualization tools provide flexible reports and dashboards based on predefined data models for occasional and business users. Data mining discovers immediate key business insights through intuitive techniques by analyzing raw data for analysts and power users. Expert mathematicians and statisticians develop accurate predictive models for core business issues using statistical algorithms.
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Data mining concepts
- 1. Group 7
- 2. What is Data Mining ? Mining and discovery of new information in terms of patterns or rules from vast amounts of data. The process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositoties, using pattern recognition technologies as well as statical and methematics techniques.
- 3. Why we mine Data ? Commercial View Point :- Lots of data is being collected and warehoused . Computers have become cheaper and more powerful. Competitive Pressure is Strong . Scientific View Point :- Data collected and stored at enormous speeds (GB/hour). Traditional techniques infeasible for raw data. Data mining may help scientists.
- 4. On what kind of Data...? • Relational databases • Data warehouses • Transactional databases • Advanced database systems: Object-relational Spacial and Temporal Time-series Multimedia, text WWW
- 5. What are the goals of Data mining? • Prediction e.g. sales volume, earthquakes • Identification e.g. existence of genes, system intrusions • Classification of different categories e.g. discount seeking shoppers or loyal regular shoppers in a supermarket • Optimization of limited resources such as time, space, money or materials and maximization of outputs such as sales or profits
- 6. What are the applications of Data- Mining ? ● Marketing ● Finance Analysis of consumer behavior Creditworthiness of clients Advertising campaigns Performance analysis of finance Targeted mailings investments Segmentation of Fraud detection customers, stores, or products ● Manufacturing ● Health Care Optimization of resources Discovering patterns in X-ray Optimization of manufacturing images processes Analyzing side effects of drugs Product design based on customer Effectiveness of treatments requirements
- 7. What are the present commercial tools for Data Mining ? Data to knowledge SAS Oracle data-miner Intelligent miner Clementine
- 8. How to build a data mining model? An important concept is that building a mining model is part of a larger process.
- 9. 1. Defining the problem. Clearly define the business problem.
- 10. 2. Preparing Data consolidate and clean the data that was identified in the Defining the Problem step.
- 11. 3.Exploring Data Explore the prepared data .
- 12. 4.Building Models Before you build a model, you must randomly separate the prepared data into separate training and testing datasets. You use the training dataset to build the model, and the testing dataset to test the accuracy of the model by creating prediction queries.
- 13. 5. Exploring and validating models Explore the models that you have built and test their effectiveness.
- 14. 6. Deploying and updating Deploy to a production models environment the models that performed the best.
- 15. What are the major issues in Data-Mining concept ? Mining different kinds of knowledge in databases Interactive mining of knowledge at multiple levels of abstraction Incorporation of background knowledge Data mining query languages and ad-hoc data mining Expression and visualization of data mining results Handling noise and incomplete data Pattern evaluation: the interestingness problem Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem Protection of data security, integrity, and privacy
- 16. How will be the future of Data-Mining concept? ● Active research is ongoing Neural Networks Regression Analysis Genetic Algorithms ● Data mining is used in many areas today. We cannot even begin to imagine what the future holds in its womb!
- 17. Thank You !