Data mining refers Knowledge mining from large amount of data. Also known as “Knowledge Discovery from Data” or KDD.
Basic terms & notations are described in that presentation
This is module 5 in the EDI Data Publishing training course. In this module, you will learn how to properly format a data file for publishing in the EDI Repository.
This is module 4 in the EDI Data Publishing training course. In this module, you will learn how to group your data files and other information products into a publishable unit.
Comparative study of frequent item set in data miningijpla
In this paper, we are an overview of already presents frequent item set mining algorithms. In these days
frequent item set mining algorithm is very popular but in the frequent item set mining computationally
expensive task. Here we described different process which use for item set mining, We also compare
different concept and algorithm which used for generation of frequent item set mining From the all the
types of frequent item set mining algorithms that have been developed we will compare important ones. We
will compare the algorithms and analyze their run time performance.
This is module 6 in the EDI Data Publishing training course. In this module, you will learn how to create quality metadata and be introduced to the landscape of data repositories and their functions.
The document discusses citing and linking data through various discovery services. It identifies the three main search engines for discovering data as EDI Data Search, DataONE Data Search, and Google Dataset Search. It provides instructions for creating a local data catalog on a website by linking data titles and URLs. Additionally, it promotes getting an ORCID identifier to link research profiles and notes the growing number of EDI services that help with data reuse, including ingestion scripts, APIs, notifications, and provenance tracking.
This is module 10 in the EDI Data Publishing training course. In this module, you will receive an introduction to what a data package is, how DOIs are assigned to data packages, and the repository's steps to insert a data package.
This document discusses elementary data organization, including primitive and non-primitive data types, data structures, and common data structure operations. It defines data as values assigned to entities, and information as meaningful, processed data. Primitive data types directly supported by machines are listed. Non-primitive data types require additional processing. Data structures arrange data in memory and include common examples like arrays and linked lists. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Data structures are classified as linear or non-linear based on how elements are arranged.
This is module 5 in the EDI Data Publishing training course. In this module, you will learn how to properly format a data file for publishing in the EDI Repository.
This is module 4 in the EDI Data Publishing training course. In this module, you will learn how to group your data files and other information products into a publishable unit.
Comparative study of frequent item set in data miningijpla
In this paper, we are an overview of already presents frequent item set mining algorithms. In these days
frequent item set mining algorithm is very popular but in the frequent item set mining computationally
expensive task. Here we described different process which use for item set mining, We also compare
different concept and algorithm which used for generation of frequent item set mining From the all the
types of frequent item set mining algorithms that have been developed we will compare important ones. We
will compare the algorithms and analyze their run time performance.
This is module 6 in the EDI Data Publishing training course. In this module, you will learn how to create quality metadata and be introduced to the landscape of data repositories and their functions.
The document discusses citing and linking data through various discovery services. It identifies the three main search engines for discovering data as EDI Data Search, DataONE Data Search, and Google Dataset Search. It provides instructions for creating a local data catalog on a website by linking data titles and URLs. Additionally, it promotes getting an ORCID identifier to link research profiles and notes the growing number of EDI services that help with data reuse, including ingestion scripts, APIs, notifications, and provenance tracking.
This is module 10 in the EDI Data Publishing training course. In this module, you will receive an introduction to what a data package is, how DOIs are assigned to data packages, and the repository's steps to insert a data package.
This document discusses elementary data organization, including primitive and non-primitive data types, data structures, and common data structure operations. It defines data as values assigned to entities, and information as meaningful, processed data. Primitive data types directly supported by machines are listed. Non-primitive data types require additional processing. Data structures arrange data in memory and include common examples like arrays and linked lists. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Data structures are classified as linear or non-linear based on how elements are arranged.
Introduction to data pre-processing and cleaning Matteo Manca
This document discusses data preparation and cleaning. It begins by explaining why data cleaning is important, as raw data is often incomplete, noisy, inconsistent, or not in a format suitable for analysis. The main steps of data cleaning are then outlined, including handling missing values, identifying outliers, resolving inconsistencies, and transforming data. Best practices for data cleaning like using pipelines to document the cleaning process and saving clean data files are also presented. Finally, the document introduces R and RStudio as tools that can be used for data cleaning.
A basic course on Research data management, part 4: caring for your data, or ...Leon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
A database is a collection of information that is organized so that it can be easily accessed, managed, and updated. Databases store data in one or more tables, which each contain records and fields. Some key reasons for using a database include that they allow large amounts of information to be stored efficiently, they make finding and sorting data quickly and easy, and they allow multiple users to access the same data simultaneously. Common examples of databases include those used by schools to store student records, hospitals to store patient information, and the government to store tax records. Proper security measures are important to protect database content and users from unauthorized access.
A classification of methods for frequent pattern miningIOSR Journals
This document discusses and compares several algorithms for frequent pattern mining. It analyzes algorithms such as CBT-fi, Index-BitTableFI, hierarchical partitioning, matrix-based data structure, bitwise AND, two-fold cross-validation, and binary-based semi-Apriori. Each algorithm is described and its advantages and disadvantages are discussed. The document concludes that CBT-fi outperforms other algorithms by clustering similar transactions to reduce memory usage and database scans while hierarchical partitioning and matrix-based approaches improve efficiency for large databases.
Data can come from various sources like observations, experiments, simulations, or other existing datasets. It can be in many forms like text, numbers, audio, video, or models. Data directly observed from individual units is called microdata, while compiled higher-level data is aggregate data. Statistics are numerical data that has been organized and analyzed, often in tables. A dataset consists of raw data files and related files like codebooks. Data repositories are collections of datasets for storage and discovery. Finding datasets involves considering who collected the type of data needed and searching publications, websites, libraries, or contacting researchers directly.
A basic course on Research data management, part 1: what and whyLeon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets that satisfy minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
This document discusses data mining and knowledge discovery in databases (KDD). It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the six steps of data mining: data selection, data cleaning, data transformation, data mining, evaluation of mined patterns, and knowledge presentation. Examples of data mining applications include recommendation systems on Amazon and YouTube, call detail records for detecting fraud, and targeted advertising on Facebook. Infographic models are also included to illustrate the data mining process which transforms raw data into useful knowledge.
This document discusses classification and prediction in data analysis. It defines classification as predicting categorical class labels, such as predicting if a loan applicant is risky or safe. Prediction predicts continuous numeric values, such as predicting how much a customer will spend. The document provides examples of classification, including a bank predicting loan risk and a company predicting computer purchases. It also provides an example of prediction, where a company predicts customer spending. It then discusses how classification works, including building a classifier model from training data and using the model to classify new data. Finally, it discusses decision tree induction for classification and the k-means algorithm.
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...ijsrd.com
In the development, standardization and implementation of LTE Networks based on Orthogonal Freq. Division Multiple Access (OFDMA), simulations are necessary to test as well as optimize algorithms and procedures before real time establishment. This can be done by both Physical Layer (Link-Level) and Network (System-Level) context. This paper proposes Network Simulator 3 (NS-3) which is capable of evaluating the performance of the Downlink Shared Channel of LTE networks and comparing it with available MatLab based LTE System Level Simulator performance.
The document defines key concepts and components of database systems. It discusses what a database is, the difference between data and information, and basic data structures like fields, columns, records, rows and files. It also defines a database management system (DBMS) as a collection of programs that enables users to create and maintain the database, and lists some advantages like efficient query processing and data security and integrity, as well as potential disadvantages like lack of decentralization and reporting features.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for data mining and analysis. It addresses issues like missing values, inconsistent data, and reducing data size. The key goals of data preprocessing are to handle data problems, integrate multiple data sources, and reduce data size while maintaining the same analytical results. Major tasks involve data cleaning, integration, transformation, and reduction.
The document discusses key concepts related to databases including:
- Databases store data in an organized format and support functions like storage, retrieval, modification and deletion of data.
- Fields are items of information that make up a record and have a defined data type like text, numbers, dates etc. Records are collections of related fields that make up a row of data.
- Database management systems provide software to create, organize and query the data within databases and support different data models.
The document discusses various techniques for preprocessing raw data prior to data mining or machine learning. It describes how data cleaning is used to fill in missing values, smooth noisy data by identifying outliers, and resolve inconsistencies. It also covers data integration, which combines data from multiple sources, and data transformation techniques that transform data into appropriate forms for analysis. The key steps in data preprocessing include data cleaning, integration, transformation, and reduction to handle issues like missing values, noise, inconsistencies and redundancies in raw data.
Data structures provide a way to organize and store data so that it can be used efficiently. They allow data to be processed, structured, and presented in a meaningful way to solve problems like slow searching of large datasets, limited processor speeds, and multiple simultaneous requests. Common data structures include linear structures like arrays, stacks, queues and linked lists, and non-linear structures like trees and graphs. Operations on data structures include traversing, searching, insertion, deletion, sorting, and merging.
Data Management in the context of Open Science.
Because open access become mandatory for publications and project-funded research data, it is the responsibility of each researcher to be informed and then trained in new practices.
This is module 11 in the EDI Data Publishing training course. In this module, you will learn the procedure to upload a data package to the EDI Repository.
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
A Study of Various Projected Data Based Pattern Mining Algorithmsijsrd.com
This document discusses and compares several algorithms for mining frequent patterns from transactional datasets: FP-Growth, H-mine, RELIM, and SaM. It analyzes the internal workings and performance of each algorithm. An experiment is conducted on the Mushroom dataset from the UCI repository using different minimum support thresholds. The results show that the execution times of the algorithms are generally similar, though SaM has a slightly lower time for higher support thresholds. The document provides an in-depth comparison of these frequent pattern mining algorithms.
Data warehousing is a repository of an organization's electronically stored data designed for reporting and analysis. A data warehouse uses an extract, transform, load process to integrate data from multiple sources and organize it into a dimensional model to support business intelligence needs. It provides consistent, integrated views of data across an organization to help analyze patterns and trends.
This document lists 5 topics: Ernest Nwanu, Open Source, Supply-chaining, The Steroids, and In-forming. It appears to be a list of concepts or ideas for further exploration but provides no additional context or details about each topic.
Introduction to data pre-processing and cleaning Matteo Manca
This document discusses data preparation and cleaning. It begins by explaining why data cleaning is important, as raw data is often incomplete, noisy, inconsistent, or not in a format suitable for analysis. The main steps of data cleaning are then outlined, including handling missing values, identifying outliers, resolving inconsistencies, and transforming data. Best practices for data cleaning like using pipelines to document the cleaning process and saving clean data files are also presented. Finally, the document introduces R and RStudio as tools that can be used for data cleaning.
A basic course on Research data management, part 4: caring for your data, or ...Leon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
A database is a collection of information that is organized so that it can be easily accessed, managed, and updated. Databases store data in one or more tables, which each contain records and fields. Some key reasons for using a database include that they allow large amounts of information to be stored efficiently, they make finding and sorting data quickly and easy, and they allow multiple users to access the same data simultaneously. Common examples of databases include those used by schools to store student records, hospitals to store patient information, and the government to store tax records. Proper security measures are important to protect database content and users from unauthorized access.
A classification of methods for frequent pattern miningIOSR Journals
This document discusses and compares several algorithms for frequent pattern mining. It analyzes algorithms such as CBT-fi, Index-BitTableFI, hierarchical partitioning, matrix-based data structure, bitwise AND, two-fold cross-validation, and binary-based semi-Apriori. Each algorithm is described and its advantages and disadvantages are discussed. The document concludes that CBT-fi outperforms other algorithms by clustering similar transactions to reduce memory usage and database scans while hierarchical partitioning and matrix-based approaches improve efficiency for large databases.
Data can come from various sources like observations, experiments, simulations, or other existing datasets. It can be in many forms like text, numbers, audio, video, or models. Data directly observed from individual units is called microdata, while compiled higher-level data is aggregate data. Statistics are numerical data that has been organized and analyzed, often in tables. A dataset consists of raw data files and related files like codebooks. Data repositories are collections of datasets for storage and discovery. Finding datasets involves considering who collected the type of data needed and searching publications, websites, libraries, or contacting researchers directly.
A basic course on Research data management, part 1: what and whyLeon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets that satisfy minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
This document discusses data mining and knowledge discovery in databases (KDD). It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the six steps of data mining: data selection, data cleaning, data transformation, data mining, evaluation of mined patterns, and knowledge presentation. Examples of data mining applications include recommendation systems on Amazon and YouTube, call detail records for detecting fraud, and targeted advertising on Facebook. Infographic models are also included to illustrate the data mining process which transforms raw data into useful knowledge.
This document discusses classification and prediction in data analysis. It defines classification as predicting categorical class labels, such as predicting if a loan applicant is risky or safe. Prediction predicts continuous numeric values, such as predicting how much a customer will spend. The document provides examples of classification, including a bank predicting loan risk and a company predicting computer purchases. It also provides an example of prediction, where a company predicts customer spending. It then discusses how classification works, including building a classifier model from training data and using the model to classify new data. Finally, it discusses decision tree induction for classification and the k-means algorithm.
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...ijsrd.com
In the development, standardization and implementation of LTE Networks based on Orthogonal Freq. Division Multiple Access (OFDMA), simulations are necessary to test as well as optimize algorithms and procedures before real time establishment. This can be done by both Physical Layer (Link-Level) and Network (System-Level) context. This paper proposes Network Simulator 3 (NS-3) which is capable of evaluating the performance of the Downlink Shared Channel of LTE networks and comparing it with available MatLab based LTE System Level Simulator performance.
The document defines key concepts and components of database systems. It discusses what a database is, the difference between data and information, and basic data structures like fields, columns, records, rows and files. It also defines a database management system (DBMS) as a collection of programs that enables users to create and maintain the database, and lists some advantages like efficient query processing and data security and integrity, as well as potential disadvantages like lack of decentralization and reporting features.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for data mining and analysis. It addresses issues like missing values, inconsistent data, and reducing data size. The key goals of data preprocessing are to handle data problems, integrate multiple data sources, and reduce data size while maintaining the same analytical results. Major tasks involve data cleaning, integration, transformation, and reduction.
The document discusses key concepts related to databases including:
- Databases store data in an organized format and support functions like storage, retrieval, modification and deletion of data.
- Fields are items of information that make up a record and have a defined data type like text, numbers, dates etc. Records are collections of related fields that make up a row of data.
- Database management systems provide software to create, organize and query the data within databases and support different data models.
The document discusses various techniques for preprocessing raw data prior to data mining or machine learning. It describes how data cleaning is used to fill in missing values, smooth noisy data by identifying outliers, and resolve inconsistencies. It also covers data integration, which combines data from multiple sources, and data transformation techniques that transform data into appropriate forms for analysis. The key steps in data preprocessing include data cleaning, integration, transformation, and reduction to handle issues like missing values, noise, inconsistencies and redundancies in raw data.
Data structures provide a way to organize and store data so that it can be used efficiently. They allow data to be processed, structured, and presented in a meaningful way to solve problems like slow searching of large datasets, limited processor speeds, and multiple simultaneous requests. Common data structures include linear structures like arrays, stacks, queues and linked lists, and non-linear structures like trees and graphs. Operations on data structures include traversing, searching, insertion, deletion, sorting, and merging.
Data Management in the context of Open Science.
Because open access become mandatory for publications and project-funded research data, it is the responsibility of each researcher to be informed and then trained in new practices.
This is module 11 in the EDI Data Publishing training course. In this module, you will learn the procedure to upload a data package to the EDI Repository.
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
A Study of Various Projected Data Based Pattern Mining Algorithmsijsrd.com
This document discusses and compares several algorithms for mining frequent patterns from transactional datasets: FP-Growth, H-mine, RELIM, and SaM. It analyzes the internal workings and performance of each algorithm. An experiment is conducted on the Mushroom dataset from the UCI repository using different minimum support thresholds. The results show that the execution times of the algorithms are generally similar, though SaM has a slightly lower time for higher support thresholds. The document provides an in-depth comparison of these frequent pattern mining algorithms.
Data warehousing is a repository of an organization's electronically stored data designed for reporting and analysis. A data warehouse uses an extract, transform, load process to integrate data from multiple sources and organize it into a dimensional model to support business intelligence needs. It provides consistent, integrated views of data across an organization to help analyze patterns and trends.
This document lists 5 topics: Ernest Nwanu, Open Source, Supply-chaining, The Steroids, and In-forming. It appears to be a list of concepts or ideas for further exploration but provides no additional context or details about each topic.
Gli alunni della 3B della scuola secondaria Prandoni di Torno illustrano il progetto ActionT4 sull'eutanasia dei disabili durante il Terzo Reich e commentano il libro di Mireille Horsinga Renno " Una ragionevole strage"
The document summarizes the mission, vision, and core values of Calayan Educational Foundation, Inc. (CEFI). The mission is to holistically develop students through responsive programs that support regional, national, and global needs. The vision is for CEFI to be a center of excellence in its disciplines, a leader in community services, and a pioneer in research. Core values include honor, scholarship, and service. CEFI offers primary to college education and is located in Lucena City.
The document provides information for students new to Austin about things to know and do in the city before graduating. It highlights Austin's history and name change, accolades as a capital city, famous filming locations and celebrities, outdoor activities like visiting lakes and live music, cheap eats including BBQ and tacos, and things to experience on and around the Concordia campus. The bucket list at the end encourages students to join clubs, go to the lake, listen to live music, take hikes, eat from food trucks, keep Austin weird, see the bats, and serve on missions before graduating.
Tornado re brand presentation (draft)(not for reproduction)Melinda Brasher
The presentation discusses redesigning the CTX brand by updating its logo and visual identity. It analyzes the anatomy of existing branding assets and how they were modified to create new logos. Examples of the new logos are shown applied to apparel, letterhead, websites and more. Feedback will be gathered before finalizing and officially launching the new brand identity.
This photo depicts Amy wearing a faux fur coat and Starbucks top, about to push her hair behind her ear. The photographer wanted to recreate a style like Rachel Joseph's photo of Helen Flanagan in a fur coat with red lipstick. The photographer used red and green colors which clash to make the photo brighter. Taking the photo freehand allowed getting the desired angle. The fur coat textures and Amy's hair make the photo look soft, while the coat lines frame the Starbucks logo and red lips as the main focuses. The photo reminds the photographer of special winter memories with Amy and Sharna at Starbucks.
Child Evangelism Fellowship of Hawaii aims to share the gospel with every child in Hawaii before age 12. They do this through Good News Clubs led by trained volunteers in schools. Currently they only have one active club, despite there being over 200 schools in Hawaii. Their vision is based on Jesus saying to let children come to him and that accepting a child is accepting him.
Universal design aims to help all students learn universally by designing flexible lessons that provide multiple means of representation, action and expression, and engagement. This is done through careful analysis of how each student learns differently and ensuring they are involved and understanding the material. The principles of universal design include presenting information through multiple means, allowing for varied expression of knowledge, and engaging students in meaningful ways. Key components are goals, materials, teaching methods, and assessment.
Nooges is the brand-new ThinkTank leading to technovation, which is a terminology that refers to revolutionary movement of information technology for the convenience of human beings and their lives, originally initiated by Nooges. In the very core of the movement,
there were efforts from the campaign to innovate business culture which is definitely the elementary unit of economic activity to the direct involvement in IT industry developing World Contents POS Project.
This blog is dedicated to discussing the role and responsibilities of the Chief Operating Officer position at The Walt Disney Company. Recent posts cover topics like Disney's theme park expansion plans, the handling of new Disney+ and ESPN+ streaming services, and strategies for improving operations across Disney's many business segments. The blog aims to provide insight into the unique challenges of overseeing global operations for one of the largest media and entertainment conglomerates in the world.
The document discusses how the meanings of the colors in the Mexican flag have changed over time. When the flag was created in 1821, green represented independence, white represented religion, and red represented national unity. Now, green represents hope for the country, white still represents religion, and red represents the blood of national heroes who fought for independence.
MMI is a sheltered maquiladora located in El Paso, TX and Ciudad Juarez, Mexico that provides manufacturing services to various industries. It has over 150k square feet of space, 700 employees across two shifts, and an experienced multilingual staff. As a contract manufacturer, MMI offers services like cutting, sewing, assembly and more. It aims to be an extension of customers' supply chains by providing short lead times, quality production, and low delivered costs within North America. MMI also assists customers with reshoring products and optimizing their supply chains.
This document provides sample social media links for two political campaigns, including YouTube and Facebook pages for Barack Obama and Mitt Romney as well as an online store selling merchandise for Barack Obama's campaign. It includes URLs for official accounts and pages on YouTube and Facebook to engage with each candidate's campaign online as well as a link to purchase campaign gear.
In case you missed it, here is the recap on how the Cook County Land Bank plans on using data and analytical tools to help acquire, manage and transfer properties.
Dokumen tersebut membahas tentang proses fotosintesis pada tumbuhan hijau. Fotosintesis adalah proses pembuatan makanan oleh tumbuhan menggunakan bahan seperti air, karbon dioksida, dan klorofil dengan bantuan sinar matahari untuk menghasilkan karbohidrat dan oksigen.
The document provides an overview of data mining and data warehousing concepts. It defines data mining as the process of analyzing large amounts of data to identify patterns and establish relationships. A data warehouse is described as a centralized repository of integrated data from multiple sources organized by subject to support analysis and decision making. The document also outlines the typical three-tier architecture of data warehouses, including extraction of data from source systems, transformation of data in an OLAP server, and analysis of data using client tools.
A database is a collection of related data organized into tables. Data is any raw fact or statistic that can be processed to derive meaningful information. Databases are important because every decision depends on underlying data. Problems with traditional file-based data storage include inconsistency, redundancy, integrity issues, and security problems. A database management system (DBMS) addresses these problems by providing a centralized and organized system to store and access data. A database consists mainly of tables, which contain records organized into attributes that can be uniquely identified by keys.
A database is a collection of related data organized into tables. Data is any raw fact or statistic, and is important because all decisions depend on underlying data. A database management system (DBMS) is used to organize data into tables to avoid problems with file-based storage like inconsistency, redundancy, integrity issues, and security problems. It allows for concurrent access. DBMS are widely used in real-world applications like movie theaters, prisons, and banks to manage related information. A table in a database contains records organized into rows with attributes or fields forming the columns. A key uniquely identifies each record.
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
This document provides an introduction to data mining. It discusses why data mining is important given the vast amounts of data being generated. It describes what data mining is and notes that it is the process of discovering patterns in large data sets. The document outlines the different types of data that can be mined, including database data, data warehouse data, transactional data, data streams, text data, and multimedia data. It also provides examples of how data mining can be applied to analyze customer data and detect sales deviations.
The document discusses the importance of data as the third component of an information system, alongside hardware and software. It defines the differences between data, information, and knowledge, noting that data provides context and meaning when transformed into information. Finally, it provides examples of how databases help organize and relate different types of data to support analysis and decision making across various domains.
This document contains an acknowledgement section thanking the project guide, Mr. Jarnail Singh, for his guidance. It then provides an executive summary on data mining, noting that data mining involves extracting patterns and useful information from large quantities of data, and some common data mining techniques include classification, association, and sequencing. It also provides an overview of the typical steps in the knowledge discovery process, including data integration, transformation, mining, evaluation, and presentation.
This document provides an overview of business intelligence and its key components. It defines business intelligence as processes, technologies, and tools that help transform data into knowledge and plans to guide business decisions. The key components discussed include data mining, data warehousing, and data analysis. Data mining involves extracting patterns from large databases, data warehousing focuses on data storage, and data analysis is the process of inspecting, cleaning, transforming, and modeling data to support decision making.
The document discusses data mining and provides an overview of key concepts. It describes data mining as the process of discovering patterns in large data sets involving techniques like classification, clustering, association rule mining, and outlier detection. It also discusses different types of data that can be mined, including transactional data and text data. Additionally, it presents different classifications of data mining systems based on the type of data, knowledge discovered, and techniques used.
The document discusses data mining and the data mining process. It describes data mining as examining databases to find patterns in data that users may not have considered. The data mining process involves 3 steps: 1) data preparation which includes cleaning and formatting data from a data warehouse, 2) running data mining algorithms like association rules and decision trees to analyze the data, and 3) evaluating the output to interpret and present any discoveries back to users. Key data mining algorithms and implementing data mining on top of a data warehouse are also covered.
Practice best Data warehousing interview questions and answers for the best preparation of the data warehousing interview. these interview questions are very popular and asked various times in data warehousing interview.
Data science involves extracting knowledge from data to solve business problems. The data science life cycle includes defining the problem, collecting and preparing data, exploring the data, building models, and communicating results. Data preparation is an essential step that can consume 60% of a project's time. It involves cleaning, transforming, handling outliers, integrating, and reducing data. Models are built using machine learning algorithms like regression for continuous variables and classification for discrete variables. Results are visualized and communicated effectively to clients.
I'm planning to give you a detailed introduction to the concepts of the data warehouse world.
We will also see why data mining and data warehouses are closely connected to each other.
dbms rdbms book by Muhammad Sharif
Database systems handbook 4rth edition.
This book is written by Muhammad Sharif, Software Engineer in SKMCHRC Lahore.
The document provides an overview of data mining and machine learning. It defines data mining as exploring and analyzing large quantities of data to discover meaningful patterns and rules. The goal is to improve understanding of the data to help achieve other goals or improve performance. Data mining employs techniques like classification, estimation, prediction, clustering, and profiling to analyze large datasets from various domains.
This document discusses data warehousing and OLAP technology for data mining. It defines what a data warehouse is, including that it is a subject-oriented, integrated, time-variant and non-volatile collection of data to support management decision making. It also discusses data warehouse architectures like star schemas and snowflake schemas, which organize data into fact and dimension tables. Finally, it discusses OLAP and multidimensional data modeling using data cubes to enable complex analyses of data in multiple dimensions.
A Survey on Approaches for Frequent Item Set Mining on Apache HadoopIJTET Journal
This document discusses approaches for mining frequent item sets on Apache Hadoop. It begins with an introduction to data mining and association rule mining. Association rule mining involves finding frequent item sets, which are items that frequently occur together. Apache Hadoop is then introduced as a framework for distributed processing of large datasets. Several algorithms for mining frequent item sets are discussed, including Apriori, FP-Growth, and H-mine. These algorithms differ in how they generate and count candidate item sets. The document then discusses how these algorithms can be implemented on Hadoop to take advantage of its distributed and parallel processing abilities in order to efficiently mine frequent item sets from large datasets.
Efficient Estimation of Word Representations in Vector Space, by T. Mikolov et al. (2013). Continuous vector representations of words by learning its context words.
Multi-class Image Classification using deep convolutional networks on extreme...Ashis Kumar Chanda
This document summarizes research on using deep convolutional networks for multi-class image classification on a large dataset from a product image classification Kaggle competition. The dataset contains over 5 million images across 5270 categories. Several CNN models were tested including ResNet, ResNext, DenseNet, and WideResNet. WideResNet achieved the best results with over 40% accuracy, while ResNext was the slowest. Training the models required significant computing resources and time due to the large dataset size. Future work includes submitting results to the Kaggle competition after more training epochs.
Full resolution image compression with recurrent neural networksAshis Kumar Chanda
This document summarizes a presentation on full resolution image compression using recurrent neural networks. It describes the motivation as reducing file sizes while maintaining quality to enable more storage and faster transmission. The proposed method applies different recurrent units like LSTM and GRU to the encoding and additive or residual reconstruction frameworks with entropy coding. Experimental results on Kodak images show the method achieves better compression than JPEG, especially at low bit rates. However, criticisms note challenges in choosing the best architecture and comparing to other 2017 approaches.
Understanding Natural Language Queries over Relational DatabasesAshis Kumar Chanda
This document summarizes a proposed method for understanding natural language queries over relational databases. The method aims to accept natural language questions from users and help them retrieve results from a database using SQL. It works by transforming the natural language into a query tree, verifying the transformation interactively, and translating the query tree into an SQL statement. The method is evaluated based on effectiveness and usability based on a dataset from Microsoft Academic Search, showing improved results over the website. However, criticisms note that users need domain knowledge, natural language variety is not discussed in depth, and experimental results do not fully support the claims.
This document discusses software cost estimation and factors that influence productivity. It defines software cost estimation as predicting resources needed for development like effort, time and total cost. Cost components include hardware/software, travel/training, and effort costs like salaries and overheads. Productivity measures include lines of code, function points based on functionality, and object points. Factors like language, code verbosity, and system characteristics can impact productivity estimates.
This document discusses risk management in software engineering projects. It defines three categories of risks: project risks that affect schedule or resources, product risks that affect software quality or performance, and business risks that affect the developing organization. The risk management process involves identifying risks, analyzing their likelihood and consequences, planning strategies to avoid or minimize risks, and monitoring risks throughout the project. Several examples of common software project risks and their potential effects are provided.
MVC means Model View Controller. An object oriented approach to design software project work. It helps to easily modify or extend the program in future.
This chapter discusses requirements engineering, which involves establishing the services customers require from a system and the constraints it operates under. It describes different types of requirements like functional and non-functional requirements. Functional requirements specify system services and behaviors, while non-functional requirements constrain aspects like timing and development process. The chapter also covers topics like ambiguous requirements, completeness, consistency, and how requirements are documented in a software requirements specification.
UML is a modeling language used to design and visualize software systems and documents. It includes various diagram types like use case diagrams, which capture system requirements, class diagrams, which show object relationships, and sequence diagrams, which display object interactions over time. Use case diagrams represent interactions between actors and the system through use cases. Class diagrams model the static structure of systems using classes, attributes, operations and relationships. Activity diagrams show the flow of system activities and decisions within a use case scenario.
The document discusses different software development process models including waterfall, evolutionary development, incremental development, and spiral models. The waterfall model involves sequential phases of requirements, design, implementation, testing and maintenance. However, it does not handle changes well. Evolutionary and incremental models incorporate feedback loops and iterative development. The spiral model is risk-driven and guides teams to adopt elements of other models based on a project's risk assessment.
The document discusses the goals and challenges of software engineering. It notes that the goals of producing software that is correct, with minimal effort and cost, and in the least time are difficult to fully achieve. Large software involves many people and millions of dollars over many years. Real-life examples like Eclipse have over 1.35 million lines of code and cost over $54 million to develop. The document outlines why software engineering is important to avoid costly failures and ensure efficient development. It also discusses myths and the need for different approaches like formal processes for large software.
An efficient approach to mine flexible periodic patterns in time series databases.
Paper link: http://www.sciencedirect.com/science/article/pii/S0952197615001013
This document discusses secure software design and development. It begins by stating that security is the top concern in software development. It then lists 10 common security flaws to avoid, such as not strictly separating data and control instructions. Next, it discusses security principles like authentication, authorization, confidentiality, non-repudiation, and availability. It also notes that developers should model security and look for bugs. The document advocates using security modeling techniques to systematically identify vulnerabilities and address countermeasures. Finally, it lists some additional security issues to consider, such as buffer overflows, insecure configuration management, and unnecessary code, and provides references for further reading.
The document discusses sequential logic circuits and finite state machines. It covers topics like combinational vs sequential logic, what defines a finite state machine, state transition diagrams, equivalent state partitioning for minimization, and applications like computer memory and delay elements. Examples are provided of a sequential circuit and its state table, as well as the process of state minimization.
This document provides an introduction to a computer science course, including topics that will be covered such as Microsoft Office applications, number theory, flow charts, and the history of important figures in computer science like Charles Babbage, Alan Turing, and Dennis Ritchie. It also mentions entrepreneurs in the field like Salman Khan and Iraj Islam. Students are prompted with questions to ask themselves about how computers understand commands, binary and decimal numbers, and ASCII code.
Iterative deepening search (IDS) is an algorithm that combines the completeness of breadth-first search with the memory efficiency of depth-first search. IDS performs an exhaustive depth-first search, increasing the depth limit by one each iteration, until the goal is found. IDS is guaranteed to find a solution if one exists, uses less memory than breadth-first search by limiting the depth of search at each iteration, and is more efficient than depth-first search which can get stuck in infinite loops.
The presentation is based on the speech of Rajkumar Buyya on Cloud Bus Toolkit.
Amit Kumar Nath (CSE, DU) and I made this presentation to provide a brief description about some useful cloud bus toolkit, such as, Aneka, CloudSim, Broker, Cloud Maker, Workflow.
Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
1. 1 I NAME OF PRESENTER
Data Mining
Ashis Kumar Chanda
Department of Computer Science and Engineering
University of Dhaka
2. 2 I NAME OF PRESENTERCSE, DU2
Key concepts
What is Data mining
Why learn Data mining
Data type
Warehouse & OLAP
Data Cleaning, Integration
Associations, Item sets, Support, Confidence
3. 3 I NAME OF PRESENTERCSE, DU3
Data Mining
Data mining refers to Knowledge mining
from large amount of data
Also known as “Knowledge Discovery from
Data” or KDD
Target is to find a Hidden Pattern
4. 4 I NAME OF PRESENTER
We can’t get all type of information through Query
Query not support Statistical analysis
Again, we can apply artificial intelligence & find new
patterns or structures
CSE, DU4
Why learn data mining
Query provide values but data mining provides idea that help
to take (business ) decision
Ex: Women live at “Dhanmondi” & older than 40 years
most frequently buy “Jamdani Shari” at “Arong”
5. 5 I NAME OF PRESENTERCSE, DU5
Data type
Tabular (Transaction data) Most commonly
used
Spatial Data (Remote sensing data/
encoded data)
Tree Data ( xml )
Graphs (www, bio-molecular)
Sequence (DNA, activity log)
Text, multimedia data
6. 6 I NAME OF PRESENTERCSE, DU6
Warehouse & OLAP
Ware House
Data Source
Warehouse is an archive of information gathered from
multiple sources
Suppose a Banking database where each has a data source
that stores all transactions of that area. And all data source
will provide a clean/safe copy at Warehouse
7. 7 I NAME OF PRESENTERCSE, DU7
Warehouse & OLAP
There is several issues about Warehouse:
When and how to gather data
What schema/pattern to use
Data transformation & cleaning
How to update
“Warehouse is a collection of data marts”
Where data mart is store of data in specialized pattern
8. 8 I NAME OF PRESENTERCSE, DU8
Warehouse & OLAP
OLAP: Online Analytical Processing
OLAP tools support interactive analysis of summary Information
OLAP permits an analyst to view different summaries of
multidimensional data
Item name
Dress
Fig: Data Cube
9. 9 I NAME OF PRESENTERCSE, DU9
Data cleaning
There may be some missing data, duplicate data, dirty data
So we need to data cleaning
Some methods:
Ignore the tuple (not effective unless tuple contain many
missing attribute)
Fill missing values (time consuming)
Fill with a global value (like: unknown)
Use mean attribute
Use most probable value
11. 11 I NAME OF PRESENTERCSE, DU11
Associations & Item sets
Associations:
An associations is a rule of the form if X then Y
It is denoted as X-> Y
Example: if there is an exam then I read
Item Sets:
For any rule if X->Y & Y->X Then X, Y are called item-set
Example:
People buying school books in January also by notebook
People buying school note books in January also by book
12. 12 I NAME OF PRESENTERCSE, DU12
Support & confidence
Support:
The proportion of transactions in the data set which contains
the itemset
Confidence:
The conditional probability that an item appears in a
transaction when another item appears.
13. 13 I NAME OF PRESENTERCSE, DU13
Support & confidence
Support for {I₁,I₂}
= support_count(I1 U I2)/ |D|
= 4/9
Confidence for I1 → I2
=support_count(I1 U I2) /
support_count(I1)
= 4/6
14. 14 I NAME OF PRESENTERCSE, DU14
Association rules
Where, support count(AUB) is the number of transactions
containing the itemsets AUB, and support count(A) is the
number of transactions containing the itemset A.
•Association rules can be generated as follows:
1. For each frequent itemset l, generate all nonempty subsets
of l.
2. For every nonempty subset s of l, output the rule “s → (l-
s)” if support count(l)/support count(s) >= min_conf,
where min_conf is the minimum confidence threshold.
15. 15 I NAME OF PRESENTERCSE, DU15
Summary
Basic topics: Data mining, Data cleaning, Warehouse, OLAP
Term: Association, Item-set, Support, Confidence
16. 16 I NAME OF PRESENTERCSE, DU16
References
- Data Mining Concepts & Techniques
by J. Han & M. Kamber
- Database system Concept
by Abraham Sillberschatz, Korth, Sudarshan
- Lecture of Dr. S. Srinath
Institute of Technology at Madras, India