Data mining is the process of extracting patterns from large data sets to identify useful information. It involves applying machine learning algorithms to detect patterns in sample data and then using the learned patterns to predict future behaviors or outcomes. Data mining utilizes techniques from machine learning, statistics, databases, and visualization to analyze large datasets and discover hidden patterns. The goal of data mining is to extract useful information from large datasets and transform it into an understandable structure for further use.
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
introduction to data mining.
Data mining the practice of examining large pre-existing databases in order to generate new information.
samrat tayade,TE IT - ARMIET COLLEGE.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
introduction to data mining.
Data mining the practice of examining large pre-existing databases in order to generate new information.
samrat tayade,TE IT - ARMIET COLLEGE.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
block diagram and signal flow graph representation
Lecture2 (1).ppt
1. Introduction to Data Mining
• What is Data Mining?
• Related technologies
• Data Mining techniques
• Data Mining Goals
• Stages of data mining process
• Knowledge representation methods
• Applications
2. What is Data Mining?
• The process of extracting information to identify patterns, trends,
and useful data that would allow the business to take the data-
driven decision from huge sets of data is called Data Mining.
• Data mining is the act of automatically searching for large stores
of information to find trends and patterns that go beyond simple
analysis procedures.
• Data Mining is a process used by organizations to extract
specific data from huge databases to solve business problems.
It primarily turns raw data into useful information.
• Data mining utilizes complex mathematical algorithms for data
segments and evaluates the probability of future events. Data
Mining is also called Knowledge Discovery of Data (KDD).
3. Related Technologies
Data mining is related to many concepts. We briefly
introduce each concept and indicate how it is related to
data mining.
• Machine Learning
• DBMS
• OLAP
• Statistics
4. Machine Learning
• Machine learning is the area of AI that examines how to write programs that
can learn.
• In data mining, machine learning is often used for prediction or classification.
• Applications that typically use machine learning techniques include speech
recognition, training moving robots, classification of astronomical structures,
and game playing.
• When machine learning is applied to data mining tasks, a model is used to
represent the data (such as a graphical structure like a neural network or a
decision tree).
• During the learning process, a sample of the database is used to train the
system to properly perform the desired task.
• Then the system is applied to the general database to actually perform the
task.
5. Machine Learning
• Machine learning algorithms are divided into two types:
1. Unsupervised Learning
2. Supervised Learning
1. Unsupervised Machine Learning:
Unsupervised learning does not depend on trained data sets to predict the
results, but it utilizes direct techniques such as clustering and association in
order to predict the results.
2. Supervised Machine Learning:
Supervised learning is a learning process in which we teach or train the
machine using data which is well leveled implies that some data is already
marked with the correct responses. After that, the machine is provided with
the new sets of data so that the supervised learning algorithm analyzes the
training data and gives an accurate result.
6. OLAP
• OLAP stands for On-Line Analytic Processing.
• OLAP systems are targeted to provide more complex query
results than traditional OLTP or database systems.
• OLAP is performed on data warehouses or data marts. The
primary goal of OLAP is to support ad hoc querying needed to
support DSS.
• The multidimensional view of data is fundamental to OLAP
applications.
• OLAP tools can be classified as ROLAP or MOLAP.
• ROLAP- Relational OLAP
• MOLAP- Multidimensional OLAP
8. OLAP operations
There are several types of OLAP operations supported by OLAP tools:
• A simple query may look at a single cell within the cube [Figure (a)] .
• Slice: Look at a subcube to get more specific information. This is performed
by selecting on one dimension. As seen in Figure (c), this is looking at a
portion of the cube.
• Dice: Look at a subcube by selecting on two or more dimensions. This can be
performed by a slice on one dimension and then rotating the cube to select
on a second dimension. In Figure (d)
• Roll up (dimension reduction, aggregation): Roll up allows the user to ask
questions that move up an aggregation hierarchy. Figure (b) represents a roll
up from (a).
• Drill down: Figure (a) represents a drill down from (b). These functions allow a
user to get more detailed fact information by navigating lower in the
aggregation hierarchy.
• Visualization: Visualization allows the OLAP users to actually "see" results of
an operation.
9. DBMS
• A database is a collection of data usually associated with some
organization or enterprise.
• Schema
– e.g. (ID,Name,Address,Salary,JobNo) may be the schema for a
personnel database.
• A database management system (DBMS) is the software used to access a
database.
• Data model is used to describe the data, attributes, and relationships
among them.
– ER Model.
10. DBMS
• Transaction
• Query:
SELECT Name
FROM T
WHERE Salary > 100000
• A major difference between data mining queries and those of database
systems is the output .
• Basic database queries always output either a subset of the database or
aggregates of the data. A data mining query outputs a KDD object.
11. Statistics
• Simple statistical concepts as determining a data distribution and calculating
a mean and a variance can be viewed as data mining techniques.
• Statistical inference: Generalizing a model created from a sample of the
data to the entire dataset.
• Exploratory Data Analysis:
– Data can actually drive the creation of the model
– Opposite of traditional statistical view.
• Statistics research has produced many of the proposed data mining
algorithms.
• The difference between the data mining and statistics is data mining is
targeted to business users not to the statistician.
12. Goals of Data Mining?
• Data mining is one of the most useful techniques that help
entrepreneurs, researchers, and individuals to extract valuable
information from huge sets of data.
• Data mining Store and manage the data in a multidimensional
database system.
• Data mining Provide data access to business analysts and
information technology professionals.
• Data mining Analyze the data by application software.
• Data mining Present the data in a useful format, such as a
graph or table.