Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
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.
Top Data Mining Techniques and Their ApplicationsPromptCloud
In this presentation we have covered why data mining is important and various techniques used for data mining. Apart from that, examples of applications have been given for each technique. This presentation also explains how an enterprise can source web data via crawling services to bolster data mining models.
This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.
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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
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
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.
Top Data Mining Techniques and Their ApplicationsPromptCloud
In this presentation we have covered why data mining is important and various techniques used for data mining. Apart from that, examples of applications have been given for each technique. This presentation also explains how an enterprise can source web data via crawling services to bolster data mining models.
This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.
=> 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/
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
We are living in a world, where a vast amount of digital data which is called big data. Plus as the world becomes more and more connected via the Internet of Things (IoT). The IoT has been a major influence on the Big Data landscape. The analysis of such big data brings ahead business competition to the next level of innovation and productivity.
INTRODUCTION TO DATA MINING
This word document contain the notes of data mining. It tells the basics of data mining like what is Data mining, it's types, issues, advantages, disadvantages, applications, social implications, basis tasks and KDD process etc. While making this notes, I had taken help from different websites of google.
The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
EXPLORING DATA MINING TECHNIQUES AND ITS APPLICATIONSeditorijettcs
Dr.T.Hemalatha#1, Dr.G.Rashita Banu#2, Dr.Murtaza Ali#3
#1.Assisstant.Professor,VelsUniversity,Chennai
#2Assistant Professor,Department of HIM&T,JazanUniversity,Jasan
#3HOD, Department of HIM&T JazanUniversity,Jasan
EXPLORING DATA MINING TECHNIQUES AND ITS APPLICATIONSeditorijettcs
Dr.T.Hemalatha#1, Dr.G.Rashita Banu#2, Dr.Murtaza Ali#3
#1.Assisstant.Professor,VelsUniversity,Chennai
#2Assistant Professor,Department of HIM&T,JazanUniversity,Jasan
#3HOD, Department of HIM&T JazanUniversity,Jasan
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Governing Equations for Fundamental Aerodynamics_Anderson2010.pdf
Data mining techniques
1. Presented By:
Suraj R. Bhuyar
M.Sc.-II Computer Science
Post Graduate Department Of Computer Science
Sant Gadge Baba Amravati University,
Amravati.
2. Introduction
There is a huge amount of data available in the
Information Industry. This data is of no use until it is
converted into useful information. It is necessary to
analyze this huge amount of data and extract useful
information from it.
Extraction of information is not only the single process,
data mining also involves other processes such as Data
Cleaning, Data Integration, Data Transformation, Data
Mining, Pattern Evaluation and Data Presentation.
Once all these processes are over, we would be able to use
this information in many applications such as Fraud
detection, Market analysis, Science exploration, etc.
3. What is Data Mining?
Why Data Mining?
What is KDD Process?
On What Kind of Data?
Data Mining Techniques
Data Mining Query Language
Applications of Data Mining
4. Extraction of interesting
Patterns or Knowledge
from huge amount of data
(Knowledge Discovery
from Data)
One of the Step from KDD
process
What is Data Mining?
5. Why Data Mining?
The Explosive Growth of Data: from
terabytes to petabytes
We are drowning in data, but starving for
knowledge!
Fraud detection and detection of unusual
patterns
6. What is KDD process?
Data cleaning
to remove noise and inconsistent data
Data integration
where multiple data sources may be combined
Data selection
Related Data
Data transformation
Unified format
7. Data mining
Extract Patterns
Pattern evaluation
to identify the truly interesting patterns
representing knowledge
Knowledge presentation
Present the mined knowledge to the user
8.
9. On What kind of Data?
Relational Databases
Collection of tables
Data Warehouses
Data from different sources
Transactional Databases
Consists of a file where each record represent
transactions
Advanced Data &Applications
Multimedia, Spatial data and WWW
11. Classification
Classification is the process of predicting the class
of a new item.
Therefore to classify the new item and identify to
which class it belongs
12. Clustering
Group Data into Clusters
Similar data is grouped in the same cluster
Dissimilar data is grouped in the same cluster
13. Regression
“Regression deals with the
prediction of a value, rather
than a class.”
Regression is a data mining
function that predicts a number
For example, a regression
model could be used to predict
children's height, given their
age, weight, and other factors.
14. Association Rules
“An association algorithm creates
rules that describe how often events
have occurred together.”
Example: When a customer buys a
Computer, then 90% of the time
they will buy softwares.
15. Data Mining Query Language
A DMQL can provide the ability to supportinteractive
data mining.
Adopts SQL-like syntax
Hence, can be easily integrated with relational query
languages
16. Applications of Data Mining
Market BasketAnalysis
Market basket analysis is a modeling technique based upon a theorythat
if you buy a certain group of items you are more likely to buy another
group of items.
This information may help the retailer to know the buyer’s needsand
retailer can enhance the store’s layout
Bio Informatics
Mining biological data helps to extract useful knowledge frommassive
datasets gathered in biology, and in other related life sciences areas
Applications of data mining to bioinformatics include
gene finding, protein function inference, disease diagnosis,disease
treatment
17. Education
Data mining can be used by an institution to take accurate
decisions and also to predict the results of the student.
Learning pattern of the students can be captured and used to
develop techniques to teach them.
Customer Relationships Management (CRM)
To maintain a proper relationship with a customer a business
need to collect data and analyze the information.
With data mining technologies the collected data can be
used for analysis.