`Data mining
•Download as PPT, PDF•
1 like•595 views
This document provides an overview of data mining. It defines data mining as the process of applying computer-based methods to discover knowledge from large amounts of data. The two main components of data mining are knowledge discovery, which extracts concrete known information from data, and knowledge prediction, which uses known data to predict future trends. Some common uses of data mining include developing AI/machine learning strategies, analyzing business strategies and customer patterns, and detecting fraud and product defects. The document also outlines different data mining models, tasks, and techniques used to classify, cluster, summarize, and analyze relationships in data.
Report
Share
Report
Share
Recommended
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.
Knowledge Discovery and Data Mining
KDD is the process of automatically extracting hidden patterns from large datasets. It involves data cleaning, reduction, exploration, modeling, and interpretation to discover useful knowledge. The goal is to gain a competitive advantage by providing improved services through understanding of the data.
Knowledge Discovery in Databases
Knowledge discovery in databases involves the non-trivial extraction of implicit and previously unknown information from large amounts of data. As data and information doubles every 20 months, knowledge discovery is needed to help analyze growing data volumes and extract useful knowledge. Knowledge discovery aims to find certain, interesting, and efficient patterns in data and is related to approaches like database management, expert systems, statistics, and scientific discovery. It has many applications in fields such as science, marketing, investments, and fraud detection.
Data mining
my topic is data mining technique.. what is data mining,and its type,application are included in this presentation..
Data mining
This document discusses data mining and its applications. It begins by defining data mining as the process of discovering patterns in large amounts of data through techniques from artificial intelligence, machine learning, statistics, and databases. The overall goal is to extract useful information from data. It then describes common data mining tasks like classification, clustering, association rule learning, and regression. Examples of data mining applications given include using it in healthcare to improve patient outcomes and reduce costs, and in education to help institutions better understand and support their students.
Data Mining
Data MiningAmity University | FMS - DU | IMT | Stratford University | KKMI International Institute | AIMA | DTU
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 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.
Data mining and Machine learning expained in jargon free & lucid language
Data mining and Machine learning explained in jargon free & lucid language.
By reading one can get some intuition about what data mining and machine learning is all about
APPLY IT IN THEIR OWN WORK
Recommended
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.
Knowledge Discovery and Data Mining
KDD is the process of automatically extracting hidden patterns from large datasets. It involves data cleaning, reduction, exploration, modeling, and interpretation to discover useful knowledge. The goal is to gain a competitive advantage by providing improved services through understanding of the data.
Knowledge Discovery in Databases
Knowledge discovery in databases involves the non-trivial extraction of implicit and previously unknown information from large amounts of data. As data and information doubles every 20 months, knowledge discovery is needed to help analyze growing data volumes and extract useful knowledge. Knowledge discovery aims to find certain, interesting, and efficient patterns in data and is related to approaches like database management, expert systems, statistics, and scientific discovery. It has many applications in fields such as science, marketing, investments, and fraud detection.
Data mining
my topic is data mining technique.. what is data mining,and its type,application are included in this presentation..
Data mining
This document discusses data mining and its applications. It begins by defining data mining as the process of discovering patterns in large amounts of data through techniques from artificial intelligence, machine learning, statistics, and databases. The overall goal is to extract useful information from data. It then describes common data mining tasks like classification, clustering, association rule learning, and regression. Examples of data mining applications given include using it in healthcare to improve patient outcomes and reduce costs, and in education to help institutions better understand and support their students.
Data Mining
Data MiningAmity University | FMS - DU | IMT | Stratford University | KKMI International Institute | AIMA | DTU
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 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.
Data mining and Machine learning expained in jargon free & lucid language
Data mining and Machine learning explained in jargon free & lucid language.
By reading one can get some intuition about what data mining and machine learning is all about
APPLY IT IN THEIR OWN WORK
Data mining
The document provides an introduction to the concept of data mining, defining it as the extraction of useful patterns from large data sources through automatic or semi-automatic means. It discusses common data mining tasks like classification, clustering, prediction, and association rule mining. Examples of data mining applications are also given such as marketing, fraud detection, and scientific data analysis.
Introduction-to-Knowledge Discovery in Database
The document discusses knowledge acquisition and data mining. It begins by defining knowledge acquisition as the process of discovering useful patterns or rules in large quantities of data through automatic or semi-automatic means. It then discusses why knowledge acquisition is important due to factors like data explosion and competitive pressure. The document also discusses different types of knowledge that can be mined, including classes, clusters, associations and sequential patterns. It outlines the predictive and descriptive approaches in data mining and common tasks like classification, clustering and association rule mining. Finally, it presents the typical steps in the knowledge discovery process including data selection, pre-processing, transformation, data mining, and interpretation.
Chapter 1. Introduction
The document provides an overview of the data mining concepts and techniques course offered at the University of Illinois at Urbana-Champaign. It discusses the motivation for data mining due to abundant data collection and the need for knowledge discovery. It also describes common data mining functionalities like classification, clustering, association rule mining and the most popular algorithms used.
Introduction to Data Mining
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
Data mining
The document provides an introduction to data mining concepts. It discusses how data mining can be used to extract useful patterns and relationships from large datasets. It explains the differences between supervised and unsupervised learning, and gives examples of classification and clustering. The document also compares various data mining techniques and algorithms such as decision trees, k-means clustering, and neural networks.
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...!!!
Introduction to Data Mining
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Datamining - On What Kind of Data
This document discusses different types of databases that can be mined for data including relational databases, data warehouses, transactional databases, and more advanced databases like object relational databases, temporal databases, spatial databases, text databases, multimedia databases, heterogeneous databases, legacy databases, data streams, and the World Wide Web. For each database type, it provides a brief definition and discusses how data mining can be applied to uncover patterns, trends, or other useful information from the data stored within.
Basic Overview of Data Mining
A companion slide deck for this chapter:
Stanton, J. M. (2013). Data Mining: A Practical Introduction for Organizational Researchers. In Cortina, J. M., & Landis, R. S., Modern Research Methods for the Study of Behavior in Organizations. New York: Routledge Academic.
Additional themes of data mining for Msc CS
Data mining involves using computational techniques from machine learning, statistics, and database systems to discover patterns in large data sets. There are several theoretical foundations of data mining including data reduction, data compression, pattern discovery, probability theory, and inductive databases. Statistical techniques like regression, generalized linear models, analysis of variance, and time series analysis are also used for statistical data mining. Visual data mining integrates data visualization techniques with data mining to discover implicit knowledge. Audio data mining uses audio signals to represent data mining patterns and results. Collaborative filtering is commonly used for product recommendations based on opinions of other customers. Privacy and security of personal data are important social concerns of data mining.
Data mining services
=> Data Mining Services
We are a full service data mining company. We handle projects both large and small, with the help of competent staff which is able to address any of the data mining needs of your company.
- Web Data Mining
- Social Media Data Mining
- SQL Data Mining
- Image Data Mining
- Excel Data Mining
- Word Data Mining
- PDF Data Mining
- Open Source Data Mining
Website: http://datacleaningservices.com/
Data mining and knowledge discovery
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,
Data mining
The document discusses data mining concepts, processes, and applications in libraries. It defines data mining as extracting patterns from large datasets using statistics and artificial intelligence. Key concepts discussed include data warehouses, metadata, and common metadata schemas. Processes of data mining outlined are creating databases, integrating data, formatting, organizing, naming data, searching/retrieving information. The need for data mining in libraries is due to huge quantities of information and the ability to satisfy user needs through better storage and retrieval systems.
What is Data mining? Data mining Presentation
Data mining involves discovering knowledge from large amounts of data through processes like extraction, cleansing, integration, transformation, and analysis. It aims to extract useful information for purposes like market analysis, risk analysis, detecting patterns, and improving websites. Key techniques include association rule mining to analyze customer purchasing patterns, classification to categorize and predict items, and clustering to group similar objects together.
What Is DATA MINING(INTRODUCTION)
This document provides an overview of data mining concepts and techniques. It defines data mining as the extraction of interesting and useful patterns from large amounts of data. The document outlines several potential applications of data mining, including market analysis, risk analysis, and fraud detection. It also describes the typical steps involved in a data mining process, including data cleaning, pattern evaluation, and knowledge presentation. Finally, it discusses different data mining functionalities, such as classification, clustering, and association rule mining.
Data Mining Overview
This document discusses the evolution of database technology and data mining. It provides a brief history of databases from the 1960s to the 2010s and their purposes over time. It then discusses the motivation for data mining, noting the explosion in data collection and need to extract useful knowledge from large databases. The rest of the document defines data mining, outlines the basic process, discusses common techniques like classification and clustering, and provides examples of data mining applications in industries like telecommunications, finance, and retail.
Data Mining: Application and trends in data mining
This document provides an overview of application trends in data mining. It discusses how data mining is used for financial data analysis, customer analysis in retail and telecommunications, biological data analysis, scientific research, intrusion detection, and more. It also outlines statistical and visualization techniques used in data mining as well as privacy and security considerations. The document concludes by encouraging the reader to explore additional self-help tutorials on data mining tools and techniques.
knowledge discovery and data mining approach in databases (2)
This document provides an overview of knowledge discovery and data mining in databases. It discusses how knowledge discovery in databases is the process of finding useful knowledge from large datasets, with data mining being the core step that extracts patterns from data. The document outlines the common steps in the knowledge discovery process, including data preparation, data mining algorithm selection and employment, pattern evaluation, and incorporating discovered knowledge. It also describes different data mining techniques such as prediction, classification, and clustering and their goals of extracting meaningful information from data.
Introduction data mining
This document provides an introduction to data mining, including its motivation, definition, applications, and key concepts. It discusses how the explosion of data has created a need for data mining to extract useful knowledge from large databases. Data mining involves techniques from machine learning, statistics, database technology, and information visualization to discover patterns in data. It can be used for applications like market analysis, risk assessment, and fraud detection. The document outlines the typical steps of the data mining process and different data mining functionalities, such as classification, clustering, and association rule mining. It also notes that not all patterns discovered will be interesting to users.
Datamining and Business Analytics
Data mining is one of the most talked about topics today, yet many companies are only scratching the surface of what it can mean for their business. In this talk, Dr. Bikram Ghosh will discuss:
What data mining is
How it is used
Techniques you can leverage
Implementation strategies to increase differentiation for your brand
Part1
This document provides an overview and introduction to the course CIS 674 Introduction to Data Mining. It defines data mining, outlines the basic data mining tasks of classification, regression, clustering, summarization and link analysis. It discusses the relationship between data mining and knowledge discovery in databases (KDD) and describes some common issues in data mining such as handling large datasets, high dimensionality, interpretation and visualization of results.
Data Mining
Data mining is the process of analyzing data from different perspectives to extract useful information that can be used to increase revenue or reduce costs. It allows companies to analyze large datasets to find relationships between internal factors like price and staffing and external factors like customer demographics and economic indicators. Data mining software extracts, transforms, and loads transaction data into a data warehouse where it can then be stored, accessed, analyzed using various techniques, and presented in a useful format to users. Common techniques include classification, clustering, association rule mining, and sequential pattern analysis.
More Related Content
What's hot
Data mining
The document provides an introduction to the concept of data mining, defining it as the extraction of useful patterns from large data sources through automatic or semi-automatic means. It discusses common data mining tasks like classification, clustering, prediction, and association rule mining. Examples of data mining applications are also given such as marketing, fraud detection, and scientific data analysis.
Introduction-to-Knowledge Discovery in Database
The document discusses knowledge acquisition and data mining. It begins by defining knowledge acquisition as the process of discovering useful patterns or rules in large quantities of data through automatic or semi-automatic means. It then discusses why knowledge acquisition is important due to factors like data explosion and competitive pressure. The document also discusses different types of knowledge that can be mined, including classes, clusters, associations and sequential patterns. It outlines the predictive and descriptive approaches in data mining and common tasks like classification, clustering and association rule mining. Finally, it presents the typical steps in the knowledge discovery process including data selection, pre-processing, transformation, data mining, and interpretation.
Chapter 1. Introduction
The document provides an overview of the data mining concepts and techniques course offered at the University of Illinois at Urbana-Champaign. It discusses the motivation for data mining due to abundant data collection and the need for knowledge discovery. It also describes common data mining functionalities like classification, clustering, association rule mining and the most popular algorithms used.
Introduction to Data Mining
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
Data mining
The document provides an introduction to data mining concepts. It discusses how data mining can be used to extract useful patterns and relationships from large datasets. It explains the differences between supervised and unsupervised learning, and gives examples of classification and clustering. The document also compares various data mining techniques and algorithms such as decision trees, k-means clustering, and neural networks.
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...!!!
Introduction to Data Mining
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Datamining - On What Kind of Data
This document discusses different types of databases that can be mined for data including relational databases, data warehouses, transactional databases, and more advanced databases like object relational databases, temporal databases, spatial databases, text databases, multimedia databases, heterogeneous databases, legacy databases, data streams, and the World Wide Web. For each database type, it provides a brief definition and discusses how data mining can be applied to uncover patterns, trends, or other useful information from the data stored within.
Basic Overview of Data Mining
A companion slide deck for this chapter:
Stanton, J. M. (2013). Data Mining: A Practical Introduction for Organizational Researchers. In Cortina, J. M., & Landis, R. S., Modern Research Methods for the Study of Behavior in Organizations. New York: Routledge Academic.
Additional themes of data mining for Msc CS
Data mining involves using computational techniques from machine learning, statistics, and database systems to discover patterns in large data sets. There are several theoretical foundations of data mining including data reduction, data compression, pattern discovery, probability theory, and inductive databases. Statistical techniques like regression, generalized linear models, analysis of variance, and time series analysis are also used for statistical data mining. Visual data mining integrates data visualization techniques with data mining to discover implicit knowledge. Audio data mining uses audio signals to represent data mining patterns and results. Collaborative filtering is commonly used for product recommendations based on opinions of other customers. Privacy and security of personal data are important social concerns of data mining.
Data mining services
=> Data Mining Services
We are a full service data mining company. We handle projects both large and small, with the help of competent staff which is able to address any of the data mining needs of your company.
- Web Data Mining
- Social Media Data Mining
- SQL Data Mining
- Image Data Mining
- Excel Data Mining
- Word Data Mining
- PDF Data Mining
- Open Source Data Mining
Website: http://datacleaningservices.com/
Data mining and knowledge discovery
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,
Data mining
The document discusses data mining concepts, processes, and applications in libraries. It defines data mining as extracting patterns from large datasets using statistics and artificial intelligence. Key concepts discussed include data warehouses, metadata, and common metadata schemas. Processes of data mining outlined are creating databases, integrating data, formatting, organizing, naming data, searching/retrieving information. The need for data mining in libraries is due to huge quantities of information and the ability to satisfy user needs through better storage and retrieval systems.
What is Data mining? Data mining Presentation
Data mining involves discovering knowledge from large amounts of data through processes like extraction, cleansing, integration, transformation, and analysis. It aims to extract useful information for purposes like market analysis, risk analysis, detecting patterns, and improving websites. Key techniques include association rule mining to analyze customer purchasing patterns, classification to categorize and predict items, and clustering to group similar objects together.
What Is DATA MINING(INTRODUCTION)
This document provides an overview of data mining concepts and techniques. It defines data mining as the extraction of interesting and useful patterns from large amounts of data. The document outlines several potential applications of data mining, including market analysis, risk analysis, and fraud detection. It also describes the typical steps involved in a data mining process, including data cleaning, pattern evaluation, and knowledge presentation. Finally, it discusses different data mining functionalities, such as classification, clustering, and association rule mining.
Data Mining Overview
This document discusses the evolution of database technology and data mining. It provides a brief history of databases from the 1960s to the 2010s and their purposes over time. It then discusses the motivation for data mining, noting the explosion in data collection and need to extract useful knowledge from large databases. The rest of the document defines data mining, outlines the basic process, discusses common techniques like classification and clustering, and provides examples of data mining applications in industries like telecommunications, finance, and retail.
Data Mining: Application and trends in data mining
This document provides an overview of application trends in data mining. It discusses how data mining is used for financial data analysis, customer analysis in retail and telecommunications, biological data analysis, scientific research, intrusion detection, and more. It also outlines statistical and visualization techniques used in data mining as well as privacy and security considerations. The document concludes by encouraging the reader to explore additional self-help tutorials on data mining tools and techniques.
knowledge discovery and data mining approach in databases (2)
This document provides an overview of knowledge discovery and data mining in databases. It discusses how knowledge discovery in databases is the process of finding useful knowledge from large datasets, with data mining being the core step that extracts patterns from data. The document outlines the common steps in the knowledge discovery process, including data preparation, data mining algorithm selection and employment, pattern evaluation, and incorporating discovered knowledge. It also describes different data mining techniques such as prediction, classification, and clustering and their goals of extracting meaningful information from data.
Introduction data mining
This document provides an introduction to data mining, including its motivation, definition, applications, and key concepts. It discusses how the explosion of data has created a need for data mining to extract useful knowledge from large databases. Data mining involves techniques from machine learning, statistics, database technology, and information visualization to discover patterns in data. It can be used for applications like market analysis, risk assessment, and fraud detection. The document outlines the typical steps of the data mining process and different data mining functionalities, such as classification, clustering, and association rule mining. It also notes that not all patterns discovered will be interesting to users.
Datamining and Business Analytics
Data mining is one of the most talked about topics today, yet many companies are only scratching the surface of what it can mean for their business. In this talk, Dr. Bikram Ghosh will discuss:
What data mining is
How it is used
Techniques you can leverage
Implementation strategies to increase differentiation for your brand
What's hot (20)
Data Mining: Application and trends in data mining
Data Mining: Application and trends in data mining
knowledge discovery and data mining approach in databases (2)
knowledge discovery and data mining approach in databases (2)
Viewers also liked
Part1
This document provides an overview and introduction to the course CIS 674 Introduction to Data Mining. It defines data mining, outlines the basic data mining tasks of classification, regression, clustering, summarization and link analysis. It discusses the relationship between data mining and knowledge discovery in databases (KDD) and describes some common issues in data mining such as handling large datasets, high dimensionality, interpretation and visualization of results.
Data Mining
Data mining is the process of analyzing data from different perspectives to extract useful information that can be used to increase revenue or reduce costs. It allows companies to analyze large datasets to find relationships between internal factors like price and staffing and external factors like customer demographics and economic indicators. Data mining software extracts, transforms, and loads transaction data into a data warehouse where it can then be stored, accessed, analyzed using various techniques, and presented in a useful format to users. Common techniques include classification, clustering, association rule mining, and sequential pattern analysis.
Thesis summary knowledge discovery from academic data using association rule...
1) The document discusses knowledge discovery from academic data at Bangladesh University of Engineering and Technology (BUET) using association rule mining.
2) It aims to analyze student performance data to understand factors behind degradation, extended time to graduation, and dropout rates.
3) The key techniques used are preprocessing the relational academic database into a universal database, then applying association rule mining to discover rules relating student attributes and course performance that can provide insights.
The Art and Science of Analyzing Software Data
This document summarizes an ICSE'14 tutorial on analyzing software data. The tutorial covers various topics:
- Organization issues like talking to users to understand goals, knowing the software domain to avoid misinterpretations, questioning the data, and seeing data science as cyclic.
- Qualitative methods like discovering information needs through surveys and interviews.
- Quantitative methods like data reduction techniques and privacy-preserving sharing.
- Open issues like data instabilities, model comparisons, and ensemble techniques.
The document emphasizes understanding the user's perspective and software domain knowledge to properly analyze data and avoid incorrect conclusions. Case studies show how missing this domain knowledge led analyses down wrong paths.
The Art of Data Science
This document discusses the rise of data science as a field and career. It outlines three megatrends driving data growth: 1) increased data availability, 2) demand for insights from data, and 3) decreased costs of storage, computing and analysis. A data scientist role requires skills in development, mathematics, product knowledge, and the ability to discover unknown patterns in data and communicate actionable insights. The document promotes a local data science group that aims to share projects, cases, and job opportunities through regular meetups and hackathons.
Data Mining Techniques
Data is produced at a phenomenal rate
Our ability to store has grown
Users expect more sophisticated information
How?
Objective: Fit data to a model
Potential Result: Higher-level meta information that may not be obvious when looking at raw data
Similar terms
Exploratory data analysis
Data driven discovery
Deductive learning
Icse15 Tech-briefing Data Science
Technical Briefing, ICSE'15, Florence, Italy, May, 2015
Tim Menzies, Leandrom Minku, Fayola Peters
Viewers also liked (7)
Thesis summary knowledge discovery from academic data using association rule...
Thesis summary knowledge discovery from academic data using association rule...
Similar to `Data mining
Data mining
The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
2 introductory slides
This document provides an introduction to data mining techniques. It discusses how data mining emerged due to the problem of data explosion and the need to extract knowledge from large datasets. It describes data mining as an interdisciplinary field that involves methods from artificial intelligence, machine learning, statistics, and databases. It also summarizes some common data mining frameworks and processes like KDD, CRISP-DM and SEMMA.
Data mining - GDi Techno Solutions
1. Find all frequent itemsets of length 1 by scanning the database to count item occurrences.
2. Iteratively generate candidate itemsets of length k from frequent itemsets of length k-1, and prune unpromising candidates using the Apriori property.
3. Scan the database to determine truly frequent itemsets.
4. Generate association rules from frequent itemsets by adding items to the antecedent and consequent of rules if they meet minimum confidence.
Data Mining in Operating System
Data mining involves discovering patterns and knowledge from large amounts of data. It is the process of analyzing data from different perspectives and summarizing it into useful information. The goals of data mining are classification, regression, clustering, association rule learning, and sequential pattern mining. Data mining is widely used in business intelligence, scientific discovery, risk management, and marketing applications to extract useful patterns from large datasets.
data warehousing and data mining
Data mining involves analyzing large amounts of data to uncover hidden patterns and relationships. It aims to fit data to models in order to derive higher-level meta information that may not be obvious from examining the raw data alone. Some basic data mining tasks include classification, which maps data to predefined groups; regression, which maps data to prediction variables; and clustering, which groups similar data together. The goal of data mining is to discover useful information from data.
Data mining Basics and complete description onword
This document discusses data mining and provides examples of its applications. It begins by explaining why data is mined from both commercial and scientific viewpoints in order to discover useful patterns and information. It then discusses some of the challenges of data mining, such as dealing with large datasets, high dimensionality, complex data types, and distributed data sources. The document outlines common data mining tasks like classification, clustering, association rule mining, and regression. It provides real-world examples of how these techniques are used for applications like fraud detection, customer profiling, and scientific discovery.
Graph
This document provides an overview of the CS590D: Data Mining course taught by Chris Clifton. The course covers what data mining is, the data mining process, and various data mining techniques like association rule mining, classification, prediction, clustering, anomaly detection, and current research topics. The course outline lists these topics over 13 weeks. Students will complete assignments, a midterm, paper reviews and a final project. Academic integrity and acknowledging sources are also discussed.
Data Mining- Unit-I PPT (1).ppt
This document provides an introduction to data mining. It discusses the history of data mining, which began with early methods like Bayes' Theorem and regression analysis in the 1700s and 1800s. The document then covers why organizations mine data from both commercial and scientific viewpoints. It defines data mining as the extraction of useful patterns from large datasets and explains how it differs from traditional data analysis. Several common data mining tasks like classification, clustering, and association rule mining are also introduced. Finally, the document outlines the typical steps involved in a knowledge discovery process.
lec01-IntroductionToDataMining.pptx
Data mining aims to discover useful patterns from large datasets. It involves applying machine learning, statistical, and visualization techniques to extract knowledge from data. Common data mining tasks include classification, clustering, association rule mining, and anomaly detection. Data mining has applications in many domains like marketing, fraud detection, and science. However, privacy and ethical issues also need consideration with widespread use of data mining.
Data warehousing
This document provides an overview of data warehousing and data mining. It defines data warehousing as a process that transforms data from various sources into a consistent and integrated store to support analysis and decision making. Key components of a data warehouse architecture include source data, data staging, data storage, and information delivery tools. The document also describes OLAP and multidimensional analysis. It then defines data mining as the process of finding hidden patterns in data and lists some common data mining techniques like the Apriori algorithm and decision trees.
Data mining concepts
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.
what is data mining
Objective: Fit data to a model
Potential Result: Higher-level meta information that may not be obvious when looking at raw data
Similar terms
Exploratory data analysis
Data driven discovery
Deductive learning
data warehousing and data mining
Data is produced at a phenomenal rate
Our ability to store has grown
Users expect more sophisticated information
How?
Objective: Fit data to a model
Potential Result: Higher-level meta information that may not be obvious when looking at raw data
Similar terms
Exploratory data analysis
Data driven discovery
Deductive learning
Session7part1
1) Data warehousing involves consolidating data from multiple sources into a centralized location, while data mining analyzes this data to find useful patterns.
2) Typical analysis tasks include examining loan approval rates by region or determining factors that influence loan defaults.
3) Online analytical processing (OLAP) tools allow interactive analysis of aggregated data and hypothesis testing through drill-downs and pivots. Leading OLAP products include Oracle Express and Essbase.
Introduction to Data Mining for Newbies
This document provides an introduction to data mining. It defines data mining as the process of exploring and analyzing large amounts of data to discover meaningful patterns. It discusses some common data mining techniques such as classification, regression, clustering, and association rule mining. It also introduces some popular data mining tools like R, SAS Enterprise Miner, and XLMiner. Finally, it mentions some notable researchers in the field of data mining.
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...
Talk by Usama Fayyad at BigMine12 at KDD12.
Virtually all organizations are having to deal with Big Data in many contexts: marketing, operations, monitoring, performance, and even financial management. Big Data is characterized not just by its size, but by its Velocity and its Variety for which keeping up with the data flux, let alone its analysis, is challenging at best and impossible in many cases. In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will refer to example frameworks and clarify the kinds of operations where Map-Reduce (Hadoop and and its derivatives) are appropriate and the situations where other infrastructure is needed to perform segmentation, prediction, analysis, and reporting appropriately – these being the fundamental operations in predictive analytics. We will thenpay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
Что такое Data Science
Data science involves collecting and analyzing large amounts of data to discover patterns and make predictions. It is an interdisciplinary field that uses techniques from mathematics, statistics, machine learning, and domain expertise. The key steps in a data science project are to explore the data through preprocessing, visualization, and modeling techniques; build a model using methods like machine learning algorithms, clustering, or decision trees; and apply the model to make predictions or other insights. Popular tools for data science include R, Python, and packages within them for statistical analysis, machine learning, and data visualization.
DM-Unit-1-Part 1-R.pdf
1) The document discusses data mining, which is defined as extracting information from large datasets. It can be used for applications like market analysis, fraud detection, and customer retention.
2) It explains the basics of data mining including the KDD (Knowledge Discovery in Databases) process and various data mining tasks and techniques.
3) The KDD process is described as the organized procedure for discovering useful patterns from large, complex datasets through steps like data cleaning, integration, selection, transformation, mining, evaluation and presentation.
Dwdmunit1 a
The document provides an overview of data mining and data warehousing concepts through a series of lectures. It discusses the evolution of database technology and data analysis, defines data mining and knowledge discovery, describes data mining functionalities like classification and clustering, and covers data warehouse concepts like dimensional modeling and OLAP operations. It also presents sample queries in a proposed data mining query language.
DataScienceIntroduction.pptx
The document discusses key concepts related to data science including:
- The difference between data and information
- An overview of the volume, velocity, variety, and veracity (4 V's) of big data
- The steps involved in data analytics from data collection to model building
- Applications of data science in various industries like retail, manufacturing, and insurance
Similar to `Data mining (20)
Data mining Basics and complete description onword
Data mining Basics and complete description onword
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...
Recently uploaded
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS Template 2023-2024 by: Irene S. Rueco
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar KanvariaHow to Setup Warehouse & Location in Odoo 17 Inventory
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
How to Add Chatter in the odoo 17 ERP Module
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.Recently uploaded (20)
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Pride Month Slides 2024 David Douglas School District
Pride Month Slides 2024 David Douglas School District
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
`Data mining
- 1. DATA MINING BY SARANYA Page 1
- 2. INTRODUCTION • New buzzword, old idea. • Inferring new information from already collected data. • Traditionally job of Data Analysts • Computers have changed this. Far more efficient to comb through data using a machine than eyeballing statistical data. Page 2
- 3. DEFINITION “Data mining is the entire process of applying computer- based methodology, including new techniques for knowledge discovery, from data.” Page 3
- 4. Two Main Components Knowledge Discovery Concrete information gleaned from known data. Data you may not have known, but which is supported by recorded facts. Knowledge Prediction Uses known data to forecast future trends, events, etc. (ie: Stock market predictions) Page 4
- 5. Uses of Data Mining • AI/Machine Learning Combinatorial/Game Data Mining Good for analyzing winning strategies to games, and thus developing intelligent AI opponents. (ie: Chess) • Business Strategies Market Basket Analysis Identify customer demographics, preferences, and purchasing patterns. • Risk Analysis Product Defect Analysis Analyze product defect rates for given plants and predict possible complications (read: lawsuits) down the line. Page 5
- 6. (Continued) • User Behavior Validation Fraud Detection In the realm of cell phones Comparing phone activity to calling records. Can help detect calls made on cloned phones. Similarly, with credit cards, comparing purchases with historical purchases. Can detect activity with stolen cards. Page 6
- 7. Sources of Data for Mining Databases (most obvious) Text Documents Computer Simulations Social Networks Page 7
- 8. Data Mining Development Page 8
- 9. Database Processing vs. Data Mining Processing • Query • Query – Well defined – Poorly defined – SQL – No precise query language • Data • Data -Operational data – - Not operational data • Output • Output - Precise – - Fuzzy - Subset of database – - Not a subset of database Page 9
- 10. Data Mining Models and Tasks Page 10
- 11. Basic Data Mining Tasks • Classification maps data into predefined groups or classes – Supervised learning – Pattern recognition – Prediction • Regression is used to map a data item to a real valued prediction variable. • Clustering groups similar data together into clusters. – Unsupervised learning – Segmentation – Partitioning Page 11
- 12. (cont’d) • Summarization maps data into subsets with associated simple descriptions. – Characterization – Generalization • Link Analysis uncovers relationships among data. – Affinity Analysis – Association Rules – Sequential Analysis determines sequential patterns. Page 12
- 13. Data Mining Techniques • Statistical – Point Estimation – Models Based on Summarization – Bayes Theorem – Hypothesis Testing – Regression and Correlation • Similarity Measures • Decision Trees • Neural Networks – Activation Functions • Genetic Algorithms Page 13
- 14. Challenges of Data Mining q Scalability q Dimensionality q Complex and Heterogeneous Data q Data Quality q Data Ownership and Distribution q Privacy Preservation q Streaming Data Page 14
- 15. THANK”U” Page 15