This document discusses data mining and different types of data mining techniques. It defines data mining as the process of analyzing large amounts of data to discover patterns and relationships. The document describes predictive data mining, which makes predictions based on historical data, and descriptive data mining, which identifies patterns and relationships. It also discusses classification, clustering, time-series analysis, and data summarization as specific data mining techniques.
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
This document provides an introduction to data mining. It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the typical steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes some common challenges in data mining like dealing with large, high-dimensional, heterogeneous and distributed data. Finally, it summarizes several common data mining tasks like classification, association analysis, clustering, and anomaly detection.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
This document provides an overview of data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
The document provides an overview of data mining concepts and techniques. It introduces data mining, describing it as the process of discovering interesting patterns or knowledge from large amounts of data. It discusses why data mining is necessary due to the explosive growth of data and how it relates to other fields like machine learning, statistics, and database technology. Additionally, it covers different types of data that can be mined, functionalities of data mining like classification and prediction, and classifications of data mining systems.
A data warehouse is a database that collects and manages data from various sources to provide business insights. It contains consolidated historical data kept separately from operational databases. A data warehouse helps executives analyze data to make strategic decisions. Data mining extracts valuable patterns and knowledge from large amounts of data through techniques like classification, clustering, and neural networks. It is used along with data warehouses for applications like churn analysis, fraud detection, and market segmentation.
Data Mining: Mining ,associations, and correlationsDatamining Tools
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 based on 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 different types of data mining techniques. It defines data mining as the process of analyzing large amounts of data to discover patterns and relationships. The document describes predictive data mining, which makes predictions based on historical data, and descriptive data mining, which identifies patterns and relationships. It also discusses classification, clustering, time-series analysis, and data summarization as specific data mining techniques.
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
This document provides an introduction to data mining. It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the typical steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes some common challenges in data mining like dealing with large, high-dimensional, heterogeneous and distributed data. Finally, it summarizes several common data mining tasks like classification, association analysis, clustering, and anomaly detection.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
This document provides an overview of data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
The document provides an overview of data mining concepts and techniques. It introduces data mining, describing it as the process of discovering interesting patterns or knowledge from large amounts of data. It discusses why data mining is necessary due to the explosive growth of data and how it relates to other fields like machine learning, statistics, and database technology. Additionally, it covers different types of data that can be mined, functionalities of data mining like classification and prediction, and classifications of data mining systems.
A data warehouse is a database that collects and manages data from various sources to provide business insights. It contains consolidated historical data kept separately from operational databases. A data warehouse helps executives analyze data to make strategic decisions. Data mining extracts valuable patterns and knowledge from large amounts of data through techniques like classification, clustering, and neural networks. It is used along with data warehouses for applications like churn analysis, fraud detection, and market segmentation.
Data Mining: Mining ,associations, and correlationsDatamining Tools
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 based on 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
The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.
This document discusses association rule mining. Association rule mining finds frequent patterns, associations, correlations, or causal structures among items in transaction databases. The Apriori algorithm is commonly used to find frequent itemsets and generate association rules. It works by iteratively joining frequent itemsets from the previous pass to generate candidates, and then pruning the candidates that have infrequent subsets. Various techniques can improve the efficiency of Apriori, such as hashing to count itemsets and pruning transactions that don't contain frequent itemsets. Alternative approaches like FP-growth compress the database into a tree structure to avoid costly scans and candidate generation. The document also discusses mining multilevel, multidimensional, and quantitative association rules.
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Pgp-Pretty Good Privacy is the open source freely available tool to encrypt your emails then you can very securely send mails to others over internet without fear of eavesdropping by cryptanalyst.
This document provides an overview of data warehousing. It defines data warehousing as collecting data from multiple sources into a central repository for analysis and decision making. The document outlines the history of data warehousing and describes its key characteristics like being subject-oriented, integrated, and time-variant. It also discusses the architecture of a data warehouse including sources, transformation, storage, and reporting layers. The document compares data warehousing to traditional DBMS and explains how data warehouses are better suited for analysis versus transaction processing.
1. The document provides an overview of key concepts in data science and machine learning including the data science process, types of data, machine learning techniques, and Python tools used for machine learning.
2. It describes the typical 6 step data science process: setting goals, data retrieval, data preparation, exploration, modeling, and presentation.
3. Different types of data are discussed including structured, unstructured, machine-generated, graph-based, and audio/video data.
4. Machine learning techniques can be supervised, unsupervised, or semi-supervised depending on whether labeled data is used.
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.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
Data mining primitives include task-relevant data, the kind of knowledge to be mined, background knowledge such as concept hierarchies, interestingness measures, and methods for presenting discovered patterns. A data mining query specifies these primitives to guide the knowledge discovery process. Background knowledge like concept hierarchies allow mining patterns at different levels of abstraction. Interestingness measures estimate pattern simplicity, certainty, utility, and novelty to filter uninteresting results. Discovered patterns can be presented through various visualizations including rules, tables, charts, and decision trees.
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
This document discusses deadlocks, including the four conditions required for a deadlock, methods to avoid deadlocks like using safe states and Banker's Algorithm, ways to detect deadlocks using wait-for graphs and detection algorithms, and approaches to recover from deadlocks such as terminating processes or preempting resources.
Mining single dimensional boolean association rules from transactionalramya marichamy
The document discusses mining frequent itemsets and generating association rules from transactional databases. It introduces the Apriori algorithm, which uses a candidate generation-and-test approach to iteratively find frequent itemsets. Several improvements to Apriori's efficiency are also presented, such as hashing techniques, transaction reduction, and approaches that avoid candidate generation like FP-trees. The document concludes by discussing how Apriori can be applied to answer iceberg queries, a common operation in market basket analysis.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Unit 1 (Chapter-1) on data mining concepts.pptPadmajaLaksh
This document provides an introduction to data mining concepts. It discusses why data mining is important due to the massive growth of data. It defines data mining as the automated analysis of large datasets to discover hidden patterns and unknown correlations. The document presents a multi-dimensional view of data mining, including the types of data that can be mined, the patterns that can be discovered, techniques used, and applications. It provides an overview of the key concepts in data mining.
The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.
This document discusses association rule mining. Association rule mining finds frequent patterns, associations, correlations, or causal structures among items in transaction databases. The Apriori algorithm is commonly used to find frequent itemsets and generate association rules. It works by iteratively joining frequent itemsets from the previous pass to generate candidates, and then pruning the candidates that have infrequent subsets. Various techniques can improve the efficiency of Apriori, such as hashing to count itemsets and pruning transactions that don't contain frequent itemsets. Alternative approaches like FP-growth compress the database into a tree structure to avoid costly scans and candidate generation. The document also discusses mining multilevel, multidimensional, and quantitative association rules.
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Pgp-Pretty Good Privacy is the open source freely available tool to encrypt your emails then you can very securely send mails to others over internet without fear of eavesdropping by cryptanalyst.
This document provides an overview of data warehousing. It defines data warehousing as collecting data from multiple sources into a central repository for analysis and decision making. The document outlines the history of data warehousing and describes its key characteristics like being subject-oriented, integrated, and time-variant. It also discusses the architecture of a data warehouse including sources, transformation, storage, and reporting layers. The document compares data warehousing to traditional DBMS and explains how data warehouses are better suited for analysis versus transaction processing.
1. The document provides an overview of key concepts in data science and machine learning including the data science process, types of data, machine learning techniques, and Python tools used for machine learning.
2. It describes the typical 6 step data science process: setting goals, data retrieval, data preparation, exploration, modeling, and presentation.
3. Different types of data are discussed including structured, unstructured, machine-generated, graph-based, and audio/video data.
4. Machine learning techniques can be supervised, unsupervised, or semi-supervised depending on whether labeled data is used.
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.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
Data mining primitives include task-relevant data, the kind of knowledge to be mined, background knowledge such as concept hierarchies, interestingness measures, and methods for presenting discovered patterns. A data mining query specifies these primitives to guide the knowledge discovery process. Background knowledge like concept hierarchies allow mining patterns at different levels of abstraction. Interestingness measures estimate pattern simplicity, certainty, utility, and novelty to filter uninteresting results. Discovered patterns can be presented through various visualizations including rules, tables, charts, and decision trees.
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
This document discusses deadlocks, including the four conditions required for a deadlock, methods to avoid deadlocks like using safe states and Banker's Algorithm, ways to detect deadlocks using wait-for graphs and detection algorithms, and approaches to recover from deadlocks such as terminating processes or preempting resources.
Mining single dimensional boolean association rules from transactionalramya marichamy
The document discusses mining frequent itemsets and generating association rules from transactional databases. It introduces the Apriori algorithm, which uses a candidate generation-and-test approach to iteratively find frequent itemsets. Several improvements to Apriori's efficiency are also presented, such as hashing techniques, transaction reduction, and approaches that avoid candidate generation like FP-trees. The document concludes by discussing how Apriori can be applied to answer iceberg queries, a common operation in market basket analysis.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Unit 1 (Chapter-1) on data mining concepts.pptPadmajaLaksh
This document provides an introduction to data mining concepts. It discusses why data mining is important due to the massive growth of data. It defines data mining as the automated analysis of large datasets to discover hidden patterns and unknown correlations. The document presents a multi-dimensional view of data mining, including the types of data that can be mined, the patterns that can be discovered, techniques used, and applications. It provides an overview of the key concepts in data mining.
This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated process of discovering patterns and knowledge from large data sets. The chapter outlines several key aspects of data mining, including the types of data that can be mined, the patterns that can be discovered, the technologies used, and its applications across various domains.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining has become important due to the massive growth of digital data. Data mining aims to extract useful patterns from large datasets through techniques like generalization, association analysis, classification, and cluster analysis. It can be applied to many types of data and has uses in domains such as business, science, and healthcare to gain insights and make predictions.
01Introduction to data mining chapter 1.pptadmsoyadm4
This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated extraction of meaningful patterns from large data sets, and notes it draws on techniques from machine learning, statistics, pattern recognition, and database systems. The chapter outlines different types of data that can be mined, patterns that can be discovered, and applications of data mining in various domains including business, science, and on the web.
This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated extraction of meaningful patterns from large data sets, and notes it draws on techniques from machine learning, statistics, pattern recognition, and database systems. The chapter outlines different types of data that can be mined, patterns that can be discovered, and applications of data mining in various domains including business, science, and on the web.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining has become important due to the massive growth of digital data. Data mining aims to extract useful patterns from large datasets through techniques like generalization, association analysis, classification, and cluster analysis. It can be applied to many types of data and has uses in domains such as business, science, and healthcare to help analyze data and discover useful knowledge.
The document provides an introduction to the concept of data mining. It discusses the evolution of data analysis techniques from empirical to computational to data-driven approaches. Data mining is presented as a natural evolution to analyze massive data sets and discover useful patterns. Key aspects of data mining covered include its functionality, types of data and knowledge that can be mined, major issues, and its relationship to other fields such as machine learning, statistics, and databases.
This document provides an overview of data mining concepts and techniques from the third edition of the textbook "Data Mining: Concepts and Techniques" by Jiawei Han, Micheline Kamber, and Jian Pei. It introduces why data mining is important due to the massive growth of data, defines data mining, and discusses the multi-dimensional nature of data mining including the types of data, patterns, techniques and applications. The chapter also covers data mining functions such as generalization, association analysis, classification, and cluster analysis.
The document provides an overview of the data mining concepts and techniques course offered at the University of Illinois at Urbana-Champaign. It discusses the motivation for data mining due to abundant data collection and the need for knowledge discovery. It also describes common data mining functionalities like classification, clustering, association rule mining and the most popular algorithms used.
Data mining Course
Chapter 1
Definition of Data Mining
Data Mining as an Interdisciplinary field
The process of Data Mining
Data Mining Tasks
Challenges of Data Mining
Data mining application examples
Introduction to RapidMiner
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses data mining with big data. It begins with an agenda that covers problem definition, objectives, literature review, algorithms, existing systems, advantages, disadvantages, big data characteristics, challenges, tools, and applications. It then goes on to define the problem, objectives, provide a literature review summarizing several papers, and describe the architecture, algorithms, existing systems, HACE theorem that models big data characteristics, advantages of the proposed system, challenges, and characteristics of big data. It concludes that formalizing big data analysis processes will be important as data volumes continue increasing.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining has become important due to the massive growth of data from various sources. Data mining involves knowledge discovery from large datasets using techniques from machine learning, statistics, pattern recognition and databases. The document outlines common data mining tasks like classification, regression, clustering and discusses applications in domains like fraud detection, customer churn prediction, and sky survey cataloging.
This document provides an overview of data mining. It discusses the introduction to data mining, its importance and applications. The key techniques of data mining discussed include classification, prediction, clustering, association and summarization. Examples of data mining applications mentioned are in healthcare, banking/finance, retail and web mining. The document concludes with discussing future trends in data mining involving new algorithms and data types, as well as computing resources like cloud computing.
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.
This document provides an overview of data mining including:
- Data mining techniques like classification, prediction, clustering which are used to analyze patterns in data.
- The importance of data mining for applications in fields like banking, retail, and healthcare to discover useful knowledge from large datasets.
- Issues with data mining like security, performance, and methodology challenges as well as future trends like using more advanced algorithms and computing resources to handle diverse and large datasets.
This document provides an overview of data mining. It defines data mining as the process of discovering valid, novel, useful and understandable patterns in large data sets. The document outlines the main components and process of data mining, including related fields. It discusses major data mining techniques and tasks such as classification, clustering and association rule mining. Examples of uses for data mining in areas such as business, science, health and risk analysis are also provided. Challenges and advantages of data mining are briefly covered.
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
A SURVEY ON DATA MINING IN STEEL INDUSTRIESIJCSES Journal
In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurancetechniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.
This document discusses repetition statements in Java, including while, for, and do-while loops. It provides examples of using each loop type, such as calculating the average of test grades in a class and summing even integers. The break and continue statements are also covered, along with examples of how they alter loop flow. Key aspects of counter-controlled repetition like loop counters, initialization, increment/decrement, and continuation conditions are defined.
This document provides an outline and overview of hashing and hash tables. It defines hashing as a technique for storing data to allow for fast insertion, retrieval, and deletion. A hash table uses an array and hash function to map keys to array indices. Collision resolution techniques like linear probing are discussed. The document also summarizes the Hashtable class in .NET, which uses buckets and load factor to avoid collisions. Examples of hash functions and using the Hashtable class are provided.
This document discusses graphs and their representation in code. It defines graphs as consisting of vertices and edges, with edges specified as pairs of vertices. It distinguishes between directed and undirected graphs. Key graph terms like paths, cycles, and connectivity are defined. Real-world systems that can be modeled as graphs are given as an example. The document then discusses representing vertices and edges in code, choosing an adjacency matrix to represent the edges in the graph.
The document discusses trees and binary trees as data structures. It defines what a tree is, including parts like the root, parent, child, leaf nodes. It then defines binary trees as trees where each node has no more than two children. Binary search trees are introduced as binary trees where all left descendants of a node are less than or equal to the node and all right descendants are greater. The document concludes by discussing how to build a binary search tree class with Node objects.
This document provides an outline and overview of the queue data structure. It defines a queue as a first-in, first-out (FIFO) structure where new items are added to the rear of the queue and items are removed from the front. The key queue operations of enqueue and dequeue are described. Code examples are provided for implementing a queue using a linked list structure with classes for the queue, its nodes, and methods for common queue operations like enqueue, dequeue, peek, clear, print, and search. Different types of queues like linear, circular, and double-ended queues are also mentioned.
The document provides an overview of stack data structures, including definitions and examples. It discusses key stack operations like push, pop, peek, clear, print all, and search. Code examples are given for an Employee class and Stack class implementation to demonstrate how these operations work on a stack of employee objects. The document aims to teach the fundamentals of stack data structures and provide code samples to practice stack operations.
This document provides an outline and overview of linked lists. It defines a linked list as a collection of nodes that are linked together by references to the next node. Each node contains a data field and a reference field. It describes how to implement a linked list using a self-referential class with fields for data and a reference to the next node. It then outlines common linked list operations like insertion and deletion at different positions as well as sorting and searching the linked list.
Chapter 4: basic search algorithms data structureMahmoud Alfarra
1) The document discusses two common search algorithms: sequential search and binary search. Sequential search looks at each item in a list sequentially until the target is found. Binary search works on a sorted list and divides the search space in half at each step.
2) It provides pseudocode examples of how each algorithm works step-by-step to find a target value in a list or array.
3) Binary search is more efficient than sequential search when the list is sorted, as it can significantly reduce the number of comparisons needed to find the target. Sequential search is used when the list is unsorted.
Chapter 3: basic sorting algorithms data structureMahmoud Alfarra
The document provides an outline and introduction for a chapter on basic sorting algorithms, including bubble sort, selection sort, and insertion sort algorithms. It includes pseudocode examples and explanations of each algorithm. It notes that bubble sort is one of the slowest but simplest algorithms, involving values "floating" to their correct positions. Selection sort finds the smallest element and places it in the first position, then repeats to find the next smallest. Insertion sort works by moving larger elements to the right to make room for smaller elements inserted from the left.
This document is a presentation on data structures in C# by Mr. Mahmoud R. Alfarra. It introduces C# and its uses in different applications. It covers various data types in C#, calculations and logical operations, control statements like if/else and loops. The document also discusses arrays, methods, and classes in C#. It provides examples to explain concepts like selection statements, iteration, and calling methods. The presentation aims to provide an introduction to the principles of C# for learning purposes.
This document provides an introduction and outline for a course on data structures. It introduces the lecturer, Mahmoud Rafeek Alfarra, and lists his qualifications. It outlines the course objectives, resources, guidelines, assessment, and schedule. Key topics that will be covered include arrays, sorting and searching algorithms, linked lists, stacks, queues, trees and graphs. The document provides classifications of different types of data structures such as linear vs nonlinear, static vs dynamic memory allocation. It concludes with information about how students can be successful in the course.
Definition of classification
Basic principles of classification
Typical
How Does Classification Works?
Difference between Classification & Prediction.
Machine learning techniques
Decision Trees
k-Nearest Neighbors
This document is a lecture on decision making practices in Java. It identifies errors in code snippets involving if/else statements and while loops. It also contains examples to trace code with variables and determine output based on variable values. The document is in Arabic and English and presented by Mahmoud R. Alfarra on using Java and correcting errors in code involving conditional and iterative structures.
This document discusses selection statements in Java including if-else statements, nested if-else statements, blocks, and switch statements. It provides examples of using these statements to check conditions, compare values, and select different code paths. It also assigns practice problems for students to write programs using selection statements to check grades, login credentials, and print days of the week.
This document provides an introduction to object-oriented programming concepts like classes, objects, and methods in Java. It defines classes as templates that define attributes and behaviors of objects as variables and methods. Objects are instances of classes. The document explains how to declare a class with access modifiers, variables, constructors, and methods. It also demonstrates how to create objects using the new keyword and access object attributes and methods.
What is a computer?
Computer Organization
Programming languages
Java Class Libraries
Typical Java development environment
Case Study: Unified Modeling Language
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
1. Introduction to Data Mining
Mahmoud Rafeek Alfarra
http://mfarra.cst.ps
University College of Science & Technology- Khan yonis
Development of computer systems
2016
Chapter 1 – Lecture 3
2. Outline
Definition of Data Mining
Data Mining as an Interdisciplinary field
Process of Data Mining
Data Mining Tasks
Challenges of Data Mining
Data mining application examples
Introduction to RapidMiner
3. Data Mining Tasks
Data mining tasks are the kind of data
patterns that can be mined.
Data Mining functionalities are used to
specify the kind of patterns to be found in the
data mining tasks.
4. In general data mining tasks can be classified into
two categories:
Descriptive mining tasks characterize the general
properties of the data.
Predictive mining tasks perform inferences on the current
data in order to make predictions.
Data Mining Tasks
5. Most famous data mining tasks:
Classification [Predictive]
Prediction [Predictive]
Association Rules [Descriptive]
Clustering [Descriptive]
Outlier Analysis [Descriptive]
Data Mining Tasks
6. Classification
Classification is used for predictive mining tasks.
The input data for predictive modeling consists of
two types of variables:
Explanatory variables, which define the essential properties of
the data.
Target variables , whose values are to be predicted.
Classification is used to predicate the value of
discrete target variable.
8. Prediction
Similar to classification, except we are trying to predict
the value of a variable (e.g. amount of purchase),
rather than a class (e.g. purchaser or non-purchaser).
9. Association
Association Rules aims to find out the relationship
among valuables in database, resulting in deferent types
of rules.
Seek to produce a set of rules describing the set of
features that are strongly related to each others.
10. Association
Gender Age Smoker LAD% RCA%
F 52 Y 85 100
M 62 N 80 0
M 75 Y 70 80
M 73 Y 40 99
M 66 N 50 45
… … … … …
LAD%- The percentage of heat disease caused by left anterior descending coronary artery.
RCA%- The percentage of heat disease caused by right coronary artery.
Original data from a research on heart disease
11. Association
Medical Association Rules
NO. Rule
1 Gender=M∩Age≥70∩Smoker=YRCA%≥50(40%,100%)
2 Gender=F∩Age<70∩Smoker=YLAD%≥70(20%,100%)
Rule 1 indicates:40% of the cases are male, over 70 years old and have the habit of
smoking, the possibility of RCA%≥50% is 100%
Rule 2 indicates:20% of the cases are female, under 70 years old and have the habit
of smoking, the possibility of LAD%≥70% is 100%
12. Clustering
Finds groups of data pointes (clusters) so that data
points that belong to one cluster are more similar to
each other than to data points belonging to different
cluster.
13. Clustering
Document Clustering:
Goal: To find groups of documents that are similar to each
other based on the important terms appearing in them.
Approach: To identify frequently occurring terms in each
document. Form a similarity measure based on the frequencies
of different terms. Use it to cluster.
Gain: Information Retrieval can utilize the clusters to relate a
new document or search term to clustered documents.
14. Outlier Analysis
Discovers data points that are significantly different
than the rest of the data. Such points are known as
anomalies or outliers.
15. Outline
Definition of Data Mining
Data Mining as an Interdisciplinary field
Process of Data Mining
Data Mining Tasks
Challenges of Data Mining
Data mining application examples
Introduction to RapidMiner
16. Challenges of Data Mining
Scalability: Scalable techniques are needed
to handle the massive scale of data.
Dimensionality: Many applications may
involves a large number of dimensions (e.g.
features or attributes of data)
17. Challenges of Data Mining
Heterogeneous and Complex Data: In recent years
complicated data types such as graph-based, text-free
and structured data types are introduced. Techniques
developed for data mining must be able to handle the
heterogeneity of the data.
18. Challenges of Data Mining
Data Quality: Many data sets are imperfect due to
present of missing values and noise un the data. To
handle the imperfection, robust data mining algorithms
must be developed.
19. Challenges of Data Mining
Data Distribution: As the volume of data increases , it
is no longer possible or safe to keep all the data in the
same place. As a result, the need for distributed data
mining techniques has increased over the years.
20. Challenges of Data Mining
Privacy Preservation: While privacy intends to prevent
the disclosure of information, data mining attempts to
revel interesting knowledge about data. As a result,
there is growing interest in developing privacy-
preserving data mining algorithms.
21. Outline
Definition of Data Mining
Data Mining as an Interdisciplinary field
Process of Data Mining
Data Mining Tasks
Challenges of Data Mining
Data mining application examples
Introduction to RapidMine
22. Data mining application
Science
astronomy, bioinformatics, drug discovery, …
Business
advertising, CRM (Customer Relationship management),
investments, manufacturing, sports/entertainment, telecom, e-
Commerce, targeted marketing, health care, …
Web
search engines, web mining,…
Government
law enforcement, profiling tax cheaters,