what is data mining
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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
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The aim of this study was to synthesize triangular gold nanoparticles, and then to evaluate their capability for inhibition of Candida albicans secreted aspartyl proteinase 2(Sap2).
Materials and Methods:
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Similar to what is data mining
Data Mining Techniques
Data is produced at a phenomenal rate
Our ability to store has grown
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The document provides an overview of data mining through a series of slides presented by Tanya Mathur. It defines key concepts like data mining, knowledge discovery in databases, and basic data mining tasks. It also outlines common issues in data mining like handling large and noisy data sets. The goal is to introduce the topic of data mining and provide a high-level understanding of the process and techniques involved.
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.
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 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.
Introduction to Data Mining
This document outlines the objectives, content, evaluation, and prerequisites for a course on Knowledge Acquisition in Decision Making, which introduces students to data mining techniques and how to apply them to solve business problems using SAS Enterprise Miner and WEKA. The course covers topics such as data preprocessing, predictive modeling with decision trees and neural networks, descriptive modeling with clustering and association rules, and a project presentation. Students will be evaluated based on assignments, case studies, a project, quizzes, class participation, and a final exam.
Datamining
The document discusses data mining methods and algorithms. It defines data mining as discovering patterns from large amounts of data. It explains that data mining is needed due to data explosion and to gain knowledge from data. It lists several usage areas for data mining such as medical, retail, banking, and computer science. It outlines the typical steps in a data mining process including data cleaning, transformation, mining, and knowledge presentation. Finally, it discusses several data mining algorithms for classification, prediction, clustering, and genetic algorithms.
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.
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.
Chapter 1: Introduction to Data Mining
This document outlines a course on knowledge acquisition in decision making, including the course objectives of introducing data mining techniques and enhancing skills in applying tools like SAS Enterprise Miner and WEKA to solve problems. The course content is described, covering topics like the knowledge discovery process, predictive and descriptive modeling, and a project presentation. Evaluation includes assignments, case studies, and a final exam.
Data Warehouse and Data Mining
This document provides an overview of data mining and data warehousing. It discusses the history and evolution of databases from the 1960s to today. Data mining is defined as using automated tools to extract hidden patterns from large databases to address the problem of data explosion. Descriptive and predictive models are used in data mining. Data warehousing involves integrating data from multiple sources into a centralized database to support analysis and decision making.
Introduction to dm and dw
Data mining involves analyzing large datasets to extract hidden patterns and predictive information. It is used to discover useful information from large data repositories like data warehouses. The document discusses data mining concepts like data extraction, data warehousing, the data mining process, applications, and issues. Major trends in data mining include datafication of enterprises, use of Hadoop for large datasets, and in-database analytics for performance.
Data Mining and Knowledge
Data mining is the process of discovering patterns in large data sets and is a core part of the knowledge discovery process. It involves preprocessing, transforming, and mining data to extract useful patterns. Main data mining tasks include classification, association rule mining, clustering, sequential pattern mining, and deviation detection. The goal is to extract valid, novel, useful, and understandable patterns that can be interpreted into knowledge through an iterative and interactive process.
Lect 1 introduction
This document provides an introduction to data mining and data warehousing. It discusses how the volume of data being collected is growing exponentially in many fields due to advances in data collection technologies. It also describes how data mining can be used to extract useful knowledge and patterns from large datasets to help solve important problems. The document outlines some key techniques in data mining including classification, clustering, and association rule mining. It discusses how data mining draws from fields like machine learning, statistics, and databases to analyze large and complex datasets.
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.
Similar to what is data mining (20)
Data mining , Knowledge Discovery Process, Classification
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what is data mining
- 1. Data Mining & Data Warehousing E2MATRIX RESEARCH LAB COMPLETE THESIS & IEEE PROJECT HELP 1 E2matrix Opp Phagaara Bus Stand, Parmar Complex, Backside Axis Bank. Phagwara, Punjab, Call : +91 9041262727
- 2. Introduction Outline Define data mining Data mining vs. databases Basic data mining tasks Data mining development Data mining issues E2matrix 2 Goal: Provide an overview of data mining.
- 3. Introduction Data is produced at a phenomenal rate Our ability to store has grown Users expect more sophisticated information How? E2matrix 3 UNCOVER HIDDEN INFORMATION DATA MINING
- 4. 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 E2matrix 4
- 5. Data Mining Algorithm Objective: Fit Data to a Model Descriptive Predictive Preferential Questions Which technique to choose? ARM/Classification/Clustering Answer: Depends on what you want to do with data? Search Strategy – Technique to search the data Interface? Query Language? Efficiency E2matrix 5
- 6. Database Processing vs. Data Mining Processing Query Well defined SQL Query Poorly defined No precise query language E2matrix 6 Output – Precise – Subset of database Output – Fuzzy – Not a subset of database
- 7. Query Examples Database Data Mining E2matrix 7 – Find all customers who have purchased milk – Find all items which are frequently purchased with milk. (association rules) – Find all credit applicants with last name of Smith. – Identify customers who have purchased more than $10,000 in the last month. – Find all credit applicants who are poor credit risks. (classification) – Identify customers with similar buying habits. (Clustering)
- 8. Data Mining Models and Tasks E2matrix 8
- 9. 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 E2matrix 9