abstract, literature survey, implementaion, sample code, html code, project description, bibilography, conclusion, result, modules, uml diagrams, design, and etc.
The document provides an introduction to the concept of data mining, defining it as the extraction of useful patterns from large data sources through automatic or semi-automatic means. It discusses common data mining tasks like classification, clustering, prediction, and association rule mining. Examples of data mining applications are also given such as marketing, fraud detection, and scientific data analysis.
This document introduces data mining. It defines data mining as the process of extracting useful information from large databases. It discusses technologies used in data mining like statistics and machine learning. It also covers data mining models and tasks such as classification, regression, clustering, and forecasting. Finally, it provides an overview of the data mining process and examples of data mining tools.
This document discusses different types of data mining including object mining, spatial mining, text mining, web mining, and multimedia mining. It describes how data mining can be used to analyze object-relational and object-oriented databases by generalizing set-valued attributes, aggregating and approximating spatial and multimedia data, generalizing object identifiers and class hierarchies. The document also discusses spatial databases, spatial data mining, spatial data warehouses, mining spatial associations and co-locations, mining raster databases, multimedia databases, and approaches for multimedia data mining and analysis.
Big data comes from a variety of sources such as sensors, social media, digital pictures, purchase transactions, and cell phone GPS signals. The volume of data created each day is vast, with 2.5 quintillion bytes created daily, 90% of which has been created in just the last two years. Big data is characterized by its volume, variety, velocity and value. It requires new tools like Hadoop and MapReduce to store and analyze data across distributed systems. When dealing with big data, once complex modeling can sometimes be replaced by simple counting techniques due to the large amount of data available. Companies are beginning to generate value from big data through new insights and business models.
This document provides an overview of data mining. It introduces data mining and its goals, which include prediction, identification, classification, and optimization. The typical architecture of a data mining system is explained, including its major components. Common data mining techniques like classification, clustering, and association are also outlined. Examples are provided to illustrate techniques. The document concludes by discussing advantages and uses of data mining along with some popular data mining tools.
Data mining is the process of analyzing large amounts of data to discover patterns and extract useful information. It involves collecting data, removing noise, focusing on relevant portions, analyzing patterns, and obtaining required information. Key components of a data mining system include a data warehouse server, information repository, user interface, data mining engine, and pattern evaluation module. A data warehouse is a centralized repository that stores integrated data from multiple sources in a unified format.
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.
Big data involves large and complex data sets from multiple sources that are rapidly growing across all domains of science and engineering. The paper presents the HACE theorem to characterize big data and proposes a processing model from a data mining perspective. This data-driven model involves aggregating information sources, mining and analyzing data, modeling user interests, and considering security and privacy, while analyzing challenges in the big data revolution.
The document provides an introduction to the concept of data mining, defining it as the extraction of useful patterns from large data sources through automatic or semi-automatic means. It discusses common data mining tasks like classification, clustering, prediction, and association rule mining. Examples of data mining applications are also given such as marketing, fraud detection, and scientific data analysis.
This document introduces data mining. It defines data mining as the process of extracting useful information from large databases. It discusses technologies used in data mining like statistics and machine learning. It also covers data mining models and tasks such as classification, regression, clustering, and forecasting. Finally, it provides an overview of the data mining process and examples of data mining tools.
This document discusses different types of data mining including object mining, spatial mining, text mining, web mining, and multimedia mining. It describes how data mining can be used to analyze object-relational and object-oriented databases by generalizing set-valued attributes, aggregating and approximating spatial and multimedia data, generalizing object identifiers and class hierarchies. The document also discusses spatial databases, spatial data mining, spatial data warehouses, mining spatial associations and co-locations, mining raster databases, multimedia databases, and approaches for multimedia data mining and analysis.
Big data comes from a variety of sources such as sensors, social media, digital pictures, purchase transactions, and cell phone GPS signals. The volume of data created each day is vast, with 2.5 quintillion bytes created daily, 90% of which has been created in just the last two years. Big data is characterized by its volume, variety, velocity and value. It requires new tools like Hadoop and MapReduce to store and analyze data across distributed systems. When dealing with big data, once complex modeling can sometimes be replaced by simple counting techniques due to the large amount of data available. Companies are beginning to generate value from big data through new insights and business models.
This document provides an overview of data mining. It introduces data mining and its goals, which include prediction, identification, classification, and optimization. The typical architecture of a data mining system is explained, including its major components. Common data mining techniques like classification, clustering, and association are also outlined. Examples are provided to illustrate techniques. The document concludes by discussing advantages and uses of data mining along with some popular data mining tools.
Data mining is the process of analyzing large amounts of data to discover patterns and extract useful information. It involves collecting data, removing noise, focusing on relevant portions, analyzing patterns, and obtaining required information. Key components of a data mining system include a data warehouse server, information repository, user interface, data mining engine, and pattern evaluation module. A data warehouse is a centralized repository that stores integrated data from multiple sources in a unified format.
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.
Big data involves large and complex data sets from multiple sources that are rapidly growing across all domains of science and engineering. The paper presents the HACE theorem to characterize big data and proposes a processing model from a data mining perspective. This data-driven model involves aggregating information sources, mining and analyzing data, modeling user interests, and considering security and privacy, while analyzing challenges in the big data revolution.
Big data refers to large datasets that are too complex for traditional data processing applications. Examples include Wikipedia which contains terabytes of text and images. Big data is characterized by being automatically generated, from new sources like the internet, and not designed for easy use. Analyzing big data can provide competitive advantages through insights from hidden patterns. Tools used for big data include distributed servers, cloud computing, distributed storage, distributed processing, and high performance databases. Data mining of big data helps businesses make better decisions by discovering patterns and relationships. Applications of big data include smarter healthcare, homeland security, traffic control, and more. Risks include being overwhelmed by data, escalating costs, and privacy issues. Big data impacts IT through new job opportunities in
This document discusses data mining, including its components of knowledge discovery and prediction. It defines data mining as applying computer methods to infer new information from existing data. The document outlines different types of data mining like data dredging and relational vs. propositional data. It provides examples of how data mining is used in business, science, health, and other domains. Privacy concerns are raised, and controversies like Facebook's Beacon program are discussed.
This document defines big data and discusses techniques for integrating large and complex datasets. It describes big data as collections that are too large for traditional database tools to handle. It outlines the "3Vs" of big data: volume, velocity, and variety. It also discusses challenges like heterogeneous structures, dynamic and continuous changes to data sources. The document summarizes techniques for big data integration including schema mapping, record linkage, data fusion, MapReduce, and adaptive blocking that help address these challenges at scale.
The document discusses data mining concepts, processes, and applications in libraries. It defines data mining as extracting patterns from large datasets using statistics and artificial intelligence. Key concepts discussed include data warehouses, metadata, and common metadata schemas. Processes of data mining outlined are creating databases, integrating data, formatting, organizing, naming data, searching/retrieving information. The need for data mining in libraries is due to huge quantities of information and the ability to satisfy user needs through better storage and retrieval systems.
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
=> Data Mining Services
We are a full service data mining company. We handle projects both large and small, with the help of competent staff which is able to address any of the data mining needs of your company.
- Web Data Mining
- Social Media Data Mining
- SQL Data Mining
- Image Data Mining
- Excel Data Mining
- Word Data Mining
- PDF Data Mining
- Open Source Data Mining
Website: http://datacleaningservices.com/
This document discusses data mining and related concepts. It provides an introduction to data mining, explaining that it involves extracting useful information from large amounts of data. It then discusses the key data mining (KDD) processes of data cleaning, integration, transformation, mining, evaluation and presentation. It also covers common data mining techniques like classification, clustering, regression and association rule mining. Finally, it discusses some applications of data mining like market basket analysis, bioinformatics, education and customer relationship management.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
Data mining refers to extracting knowledge from large amounts of data and involves techniques from machine learning, statistics, and databases. A typical data mining system includes a database, data mining engine, pattern evaluation module, and graphical user interface. The knowledge discovery in data (KDD) process involves data cleaning, integration, selection, transformation, mining, evaluation, and presentation to extract useful patterns from data. KDD is the overall process while data mining is one step, applying algorithms to extract patterns for analysis.
Data mining is the process of analyzing large databases to discover useful patterns. It involves applying computer-based methods to derive knowledge from large amounts of data. The main components of data mining are knowledge discovery, where concrete information is gleaned from known data, and knowledge prediction, which uses known data to forecast future trends. Data is collected and stored in a centralized data warehouse to allow for easier querying. Common data mining techniques include classification, clustering, regression, and association rule mining. Data mining has various applications in areas such as business, science, medicine, and more to gain useful insights from data. However, effective data mining requires linking multiple data sources which can raise privacy concerns if a person's entire data history is assembled.
This document provides an overview of data mining and knowledge discovery in databases (KDD). It defines data mining as the process of extracting interesting and useful patterns from large databases. KDD is described as identifying valid and understandable patterns in data. The document outlines the differences between data, information, and knowledge, and discusses how data mining can be used to turn data into knowledge. It also summarizes some common applications of data mining such as in retail, finance, science, and recommender systems. Finally, it briefly discusses the roles of data warehouses and data cleaning in the data mining process.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining is needed due to the massive growth of data. It defines data mining as the extraction of interesting patterns from large datasets. The document outlines the key steps in the knowledge discovery process and how data mining fits within business intelligence applications. It also describes different types of data that can be mined and popular data mining algorithms.
The document discusses data mining and its processes. It states that data mining involves extracting useful information and patterns from large amounts of data through processes like data cleaning, integration, transformation, mining, and presentation. This extracted knowledge can then be applied to various domains such as fraud detection, market analysis, and science exploration.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
Abstract: Knowledge has played a significant role on human activities since his development. Data mining is the process of
knowledge discovery where knowledge is gained by analyzing the data store in very large repositories, which are analyzed
from various perspectives and the result is summarized it into useful information. Due to the importance of extracting
knowledge/information from the large data repositories, data mining has become a very important and guaranteed branch of
engineering affecting human life in various spheres directly or indirectly. The purpose of this paper is to survey many of the
future trends in the field of data mining, with a focus on those which are thought to have the most promise and applicability
to future data mining applications.
Keywords: Current and Future of Data Mining, Data Mining, Data Mining Trends, Data mining Applications.
This Is just a little overview on it not fully explaned.
Data Mining:-
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information.
Data Mining is the Process that is used by big companies or organizations to handle,balance and analyzing big data.
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factor.
It is used by the companies to increase their revenue or cut their costs or both.
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two.
Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries.
SOFTWARES FOR DATA MINING:
Microsoft SQL SERVER 2005.
Mircrsoft SQL SERVER 2008.
Oracle Data Mining etc.
One Super Market in Canada used data mining capacity of Oracle Software to analyze local buying patterns.They discovered that when Mens bought Food for home on Saturday and Sunday They like to tended to buy beer.On Other days of the week mens don’t usually buy beers.
The Shopkeeper said to his workers to put sufficient amount of beers on Saturday and Sunday.In this way income of the shop was increased.
BI refers to applications & technologies which are use to gather information about their company opertaions.
Data Mining is importand part of business intellegence.
Some Basic Examples of Use of Data Mining Are Given Below:
In Finance Data Mining is used for Credit Cards Analysis.
Astronomy:
Palomar Obstervatory discovered 22 quasars with the help of Data Mining.
3) Telecommunication:
In Telecommunication Data Mining is used for Call Records.
4) Offices:
In Offices it is used for to balance data and records of the staff. etc
Following are some of the types if Data Mining:
Assoication Rule is used for store layout. Etc.
Classification is used for weather prediction. Etc.
Clustering is used for Graphical Represention of Universe.
Sequential Pattern is used for medical diagnosis.
THANK YOU...!!!
This document discusses big data and data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional techniques due to their size. It outlines the 4 Vs of big data: volume, velocity, variety, and veracity. The proposed system would use distributed parallel computing with Hadoop to identify relationships in huge amounts of data from different sources and dimensions. It discusses challenges of big data like data location, volume, privacy, and gaining insights. Solutions involve parallel programming, distributed storage, and access restrictions.
This document discusses big data, defining it as the exponential growth and availability of both structured and unstructured data. It describes big data using the three V's: volume, velocity, and variety. It also discusses two additional dimensions of big data: variability and complexity. The document explains that analyzing big data can lead to cost reductions, time reductions, new product development, and better business decisions. It provides examples of how companies like eBay, Amazon, Walmart, and Facebook handle and analyze large amounts of data.
Big data presents challenges at the data, model, and system levels. At the data level, issues include heterogeneous sources, missing/uncertain values, and privacy/errors. At the model level, generating global models from local patterns is difficult. At the system level, linking complex relationships between data sources and handling growth is challenging. Addressing these issues requires high-performance computing, algorithms to analyze distributed data and models, and carefully designed systems to form useful patterns from unstructured data and identify trends over time. Big data technologies may help provide more accurate social sensing and understanding.
This document outlines 5 modules for an implementation: 1) integrating and mining biodata from multiple sources to understand biological networks, 2) building a Big Data analytic framework for fast response and real-time decision making by reducing data volumes, building prediction models, and ensuring real-time monitoring, 3) performing pattern matching and mining with wildcards and their applications, 4) investigating technologies for integrating and mining multisource, massive, and dynamic information, and 5) employing models of group influence and interactions in social networks to analyze emotional interactions and influence among individuals and groups.
Big data refers to large datasets that are too complex for traditional data processing applications. Examples include Wikipedia which contains terabytes of text and images. Big data is characterized by being automatically generated, from new sources like the internet, and not designed for easy use. Analyzing big data can provide competitive advantages through insights from hidden patterns. Tools used for big data include distributed servers, cloud computing, distributed storage, distributed processing, and high performance databases. Data mining of big data helps businesses make better decisions by discovering patterns and relationships. Applications of big data include smarter healthcare, homeland security, traffic control, and more. Risks include being overwhelmed by data, escalating costs, and privacy issues. Big data impacts IT through new job opportunities in
This document discusses data mining, including its components of knowledge discovery and prediction. It defines data mining as applying computer methods to infer new information from existing data. The document outlines different types of data mining like data dredging and relational vs. propositional data. It provides examples of how data mining is used in business, science, health, and other domains. Privacy concerns are raised, and controversies like Facebook's Beacon program are discussed.
This document defines big data and discusses techniques for integrating large and complex datasets. It describes big data as collections that are too large for traditional database tools to handle. It outlines the "3Vs" of big data: volume, velocity, and variety. It also discusses challenges like heterogeneous structures, dynamic and continuous changes to data sources. The document summarizes techniques for big data integration including schema mapping, record linkage, data fusion, MapReduce, and adaptive blocking that help address these challenges at scale.
The document discusses data mining concepts, processes, and applications in libraries. It defines data mining as extracting patterns from large datasets using statistics and artificial intelligence. Key concepts discussed include data warehouses, metadata, and common metadata schemas. Processes of data mining outlined are creating databases, integrating data, formatting, organizing, naming data, searching/retrieving information. The need for data mining in libraries is due to huge quantities of information and the ability to satisfy user needs through better storage and retrieval systems.
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
=> Data Mining Services
We are a full service data mining company. We handle projects both large and small, with the help of competent staff which is able to address any of the data mining needs of your company.
- Web Data Mining
- Social Media Data Mining
- SQL Data Mining
- Image Data Mining
- Excel Data Mining
- Word Data Mining
- PDF Data Mining
- Open Source Data Mining
Website: http://datacleaningservices.com/
This document discusses data mining and related concepts. It provides an introduction to data mining, explaining that it involves extracting useful information from large amounts of data. It then discusses the key data mining (KDD) processes of data cleaning, integration, transformation, mining, evaluation and presentation. It also covers common data mining techniques like classification, clustering, regression and association rule mining. Finally, it discusses some applications of data mining like market basket analysis, bioinformatics, education and customer relationship management.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
Data mining refers to extracting knowledge from large amounts of data and involves techniques from machine learning, statistics, and databases. A typical data mining system includes a database, data mining engine, pattern evaluation module, and graphical user interface. The knowledge discovery in data (KDD) process involves data cleaning, integration, selection, transformation, mining, evaluation, and presentation to extract useful patterns from data. KDD is the overall process while data mining is one step, applying algorithms to extract patterns for analysis.
Data mining is the process of analyzing large databases to discover useful patterns. It involves applying computer-based methods to derive knowledge from large amounts of data. The main components of data mining are knowledge discovery, where concrete information is gleaned from known data, and knowledge prediction, which uses known data to forecast future trends. Data is collected and stored in a centralized data warehouse to allow for easier querying. Common data mining techniques include classification, clustering, regression, and association rule mining. Data mining has various applications in areas such as business, science, medicine, and more to gain useful insights from data. However, effective data mining requires linking multiple data sources which can raise privacy concerns if a person's entire data history is assembled.
This document provides an overview of data mining and knowledge discovery in databases (KDD). It defines data mining as the process of extracting interesting and useful patterns from large databases. KDD is described as identifying valid and understandable patterns in data. The document outlines the differences between data, information, and knowledge, and discusses how data mining can be used to turn data into knowledge. It also summarizes some common applications of data mining such as in retail, finance, science, and recommender systems. Finally, it briefly discusses the roles of data warehouses and data cleaning in the data mining process.
This document provides an introduction to data mining concepts and techniques. It discusses why data mining is needed due to the massive growth of data. It defines data mining as the extraction of interesting patterns from large datasets. The document outlines the key steps in the knowledge discovery process and how data mining fits within business intelligence applications. It also describes different types of data that can be mined and popular data mining algorithms.
The document discusses data mining and its processes. It states that data mining involves extracting useful information and patterns from large amounts of data through processes like data cleaning, integration, transformation, mining, and presentation. This extracted knowledge can then be applied to various domains such as fraud detection, market analysis, and science exploration.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
Abstract: Knowledge has played a significant role on human activities since his development. Data mining is the process of
knowledge discovery where knowledge is gained by analyzing the data store in very large repositories, which are analyzed
from various perspectives and the result is summarized it into useful information. Due to the importance of extracting
knowledge/information from the large data repositories, data mining has become a very important and guaranteed branch of
engineering affecting human life in various spheres directly or indirectly. The purpose of this paper is to survey many of the
future trends in the field of data mining, with a focus on those which are thought to have the most promise and applicability
to future data mining applications.
Keywords: Current and Future of Data Mining, Data Mining, Data Mining Trends, Data mining Applications.
This Is just a little overview on it not fully explaned.
Data Mining:-
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information.
Data Mining is the Process that is used by big companies or organizations to handle,balance and analyzing big data.
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factor.
It is used by the companies to increase their revenue or cut their costs or both.
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two.
Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries.
SOFTWARES FOR DATA MINING:
Microsoft SQL SERVER 2005.
Mircrsoft SQL SERVER 2008.
Oracle Data Mining etc.
One Super Market in Canada used data mining capacity of Oracle Software to analyze local buying patterns.They discovered that when Mens bought Food for home on Saturday and Sunday They like to tended to buy beer.On Other days of the week mens don’t usually buy beers.
The Shopkeeper said to his workers to put sufficient amount of beers on Saturday and Sunday.In this way income of the shop was increased.
BI refers to applications & technologies which are use to gather information about their company opertaions.
Data Mining is importand part of business intellegence.
Some Basic Examples of Use of Data Mining Are Given Below:
In Finance Data Mining is used for Credit Cards Analysis.
Astronomy:
Palomar Obstervatory discovered 22 quasars with the help of Data Mining.
3) Telecommunication:
In Telecommunication Data Mining is used for Call Records.
4) Offices:
In Offices it is used for to balance data and records of the staff. etc
Following are some of the types if Data Mining:
Assoication Rule is used for store layout. Etc.
Classification is used for weather prediction. Etc.
Clustering is used for Graphical Represention of Universe.
Sequential Pattern is used for medical diagnosis.
THANK YOU...!!!
This document discusses big data and data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional techniques due to their size. It outlines the 4 Vs of big data: volume, velocity, variety, and veracity. The proposed system would use distributed parallel computing with Hadoop to identify relationships in huge amounts of data from different sources and dimensions. It discusses challenges of big data like data location, volume, privacy, and gaining insights. Solutions involve parallel programming, distributed storage, and access restrictions.
This document discusses big data, defining it as the exponential growth and availability of both structured and unstructured data. It describes big data using the three V's: volume, velocity, and variety. It also discusses two additional dimensions of big data: variability and complexity. The document explains that analyzing big data can lead to cost reductions, time reductions, new product development, and better business decisions. It provides examples of how companies like eBay, Amazon, Walmart, and Facebook handle and analyze large amounts of data.
Big data presents challenges at the data, model, and system levels. At the data level, issues include heterogeneous sources, missing/uncertain values, and privacy/errors. At the model level, generating global models from local patterns is difficult. At the system level, linking complex relationships between data sources and handling growth is challenging. Addressing these issues requires high-performance computing, algorithms to analyze distributed data and models, and carefully designed systems to form useful patterns from unstructured data and identify trends over time. Big data technologies may help provide more accurate social sensing and understanding.
This document outlines 5 modules for an implementation: 1) integrating and mining biodata from multiple sources to understand biological networks, 2) building a Big Data analytic framework for fast response and real-time decision making by reducing data volumes, building prediction models, and ensuring real-time monitoring, 3) performing pattern matching and mining with wildcards and their applications, 4) investigating technologies for integrating and mining multisource, massive, and dynamic information, and 5) employing models of group influence and interactions in social networks to analyze emotional interactions and influence among individuals and groups.
Big data has arrived, with 2.5 quintillion bytes of data created every day. The era of big data has emerged since the invention of information technology, and our ability to generate data has never been greater. Examples include over 10 million tweets generated during a 2012 presidential debate, and 1.8 million photos uploaded daily to Flickr. Effectively analyzing and extracting useful information from these enormous datasets in real-time presents significant challenges and will require new techniques and tools to handle the volumes and speeds of big data.
Java technology includes both a programming language and platform. The Java programming language is compiled into bytecode that can run on any Java Virtual Machine (JVM). This allows Java programs to "write once, run anywhere." The Java platform consists of the JVM and Java API libraries. The API provides functionality like GUIs, networking, security, and database connectivity. The document provides details on the Java language features, how programs are compiled and run, the Java platform architecture, and some of the capabilities provided by the Java API libraries.
Big data comes from large, heterogeneous sources with decentralized control, and seeks to understand complex relationships within the data. This scenario is like blind men trying to describe a camel based on feeling different parts of it. Big data has three key characteristics:
1) It is huge in volume and comes from diverse sources like social media.
2) The data sources have distributed and decentralized control without central oversight.
3) The data is complex with multistructure associations that require consolidation to extract maximum value.
This document contains 10 references cited in another work. The references are numbered and include the author(s), title, publication venue, and year. Topics covered include HTML5 application privilege separation, orthogonal security techniques, proxy re-encryption, database security and privacy, securing frame communication in browsers, content sniffing techniques, social network data anonymization, adapting Kerberos for browsers, and news articles regarding privacy and surveillance.
The document outlines the system configuration for a computer, including the hardware of a Pentium III processor, 256MB RAM, 20GB hard drive, 1.44MB floppy drive, standard keyboard, and SVGA monitor. The software configuration is specified as Windows 95/98/2000/XP, with Tomcat 5.0/6.X as the application server, and front-end technologies of HTML, Java, and JSP alongside JavaScript, JSP server-side scripting, MySQL 5.0 database, and JDBC database connectivity.
This very short document contains three lines that seem to list different levels or types as "Three tier", "Two tier", and "One tier". It provides very little context or detail, simply noting three terms in descending order from three to one.
Input design is the process of converting user-oriented data into a processable format for the computer system. It controls the amount of input needed, prevents errors, and avoids extra steps to make the process simple. Input is designed to be secure and easy to use while protecting privacy. Objectives of input design are to create user-friendly screens for efficient data entry, validate data for accuracy, and provide guidance and feedback to users.
System testing is done to discover errors by testing all components, subassemblies, and the final product. There are various types of tests that each address specific testing needs. Unit testing checks individual software units before integration to validate internal logic and outputs. Integration testing checks integrated components run as one program. Functional testing systematically exercises software functions, inputs, outputs, and interfaces as specified in requirements. System testing ensures the full integrated system meets requirements through configuration testing.
This document discusses the feasibility study phase of a systems analysis project. The feasibility study aims to analyze whether the proposed system is economically and technically feasible and socially acceptable. It examines the economic impacts on the organization, ensuring the system's costs are justified and within budget. Technically, the system should not place high demands on resources. Socially, users must accept and feel comfortable with the system through training and education that raises their confidence level.
The existing system faces challenges in managing and processing large volumes of data beyond the capabilities of typical software tools. It also struggles with real-time classification and analysis of big data. The proposed system addresses these issues through a HACE theorem that models big data characteristics as huge, autonomous, and complex and evolving. It proposes high-performance computing platforms to unleash the full power of big data and provide real-time and relevant social sensing feedback.
This document discusses big data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional methods due to their size. It describes the characteristics of big data including volume, variety, velocity, variability, and complexity. It also discusses challenges of big data such as data location, volume, hardware resources, and privacy. Popular tools for big data mining include Hadoop, Apache S4, Storm, Apache Mahout, and MOA. Hadoop is an open source software framework that allows distributed processing of large datasets across clusters of computers. Common algorithms for big data mining operate at the model and knowledge levels to discover patterns and correlations across distributed data sources.
There are as many views and definitions of Data Mining as there are people working in and on the topic. Confusion reigns and people ask; what is it; why do we need it; and isn’t it just Data Mining rebranded? In this slide deck and presentation we set the scene an highlight the differences and need for Data Mining in order to give a framework for case studies and future projects.
So - why do we need it?
The economic, industrial, commercial, social, political and sustainability problems we face cannot be successfully addressed using the management techniques and models largely inherited from the Industrial Revolution. The world no longer appears infinite in resources, slow paced, linear and stable. We now see the limitations; feel the impact of rapid change; and we can conceptualize the non-linear and unstable nature of it all! We are also starting to comprehend the scale and the need for machine assistance.
Modeling our situation !
Sophisticated computer models for weather systems are now complemented by ecological, economic, conflict and resource modeling of varying depth and accuracy. However, the key is always the accuracy and coverage of the primary data. We started with modest databases and data mining, but they mostly proved inadequate, and we are now amassing vast databases on every aspect of life - people, planet and machines. This ‘BIG DATA’ explosion demands a rethink of how, what, and where we gather data; the way we analyze and model; and the way we make decisions.
So - what is the big difference?
Data Mining was limited, planer, simple, linear and constrained to a few relationships amongst people: what they did, where they went, who they knew and so on. In contrast; Big Data is unbounded, spans all peoples and machines in all domains and activities with application to every aspect of life, business, industry, government and sustainability etc. It also takes into account the non-linear nature of relationships and events.
“Big Data is an almost unconscious outcome of the desire and need to sustain all peoples on a rapidly smaller looking planet”
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.
A Model Design of Big Data Processing using HACE TheoremAnthonyOtuonye
This document presents a model for big data processing using the HACE theorem. It proposes a three-tier data mining structure to provide accurate, real-time social feedback for understanding society. The model adopts Hadoop's MapReduce for big data mining and uses k-means and Naive Bayes algorithms for clustering and classification. The goal is to address challenges of big data and assist governments and businesses in using big data technology.
A Novel Framework for Big Data Processing in a Data-driven SocietyAnthonyOtuonye
This document summarizes a journal article that proposes a novel big data processing framework. It begins by defining big data and noting the rapid rise in data from sources like social media, sensors, and the internet. It then describes challenges with analyzing this large, complex data. The paper introduces a three-tier big data mining structure that analyzes data from multiple sources on a single platform and provides real-time social feedback. It adopts the HACE theorem to characterize big data's size, heterogeneity, complexity and evolving nature. The framework uses Hadoop's MapReduce for distributed parallel processing. The study aims to fully leverage big data's benefits and enhance large-scale data management and analysis for governments and businesses.
The document provides an introduction to big data and data mining. It defines big data as massive volumes of structured and unstructured data that are difficult to process using traditional techniques. Data mining is described as finding new and useful information within large amounts of data. The document then discusses characteristics of big data like volume, variety and velocity. It also outlines challenges of big data like privacy and hardware resources. Finally, it presents tools for big data mining and analysis like Hadoop, Apache S4 and Mahout.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
This document provides an overview of big data. It begins with an introduction that defines big data as massive, complex data sets from various sources that are growing rapidly in volume and variety. It then discusses the brief history of big data and provides definitions, describing big data as data that is too large and complex for traditional data management tools. The document outlines key aspects of big data including the sources, types, applications, and characteristics. It discusses how big data is used in business intelligence to help companies make better decisions. Finally, it describes the key aspects a big data platform must address such as handling different data types, large volumes, and analytics.
ISSUES, CHALLENGES, AND SOLUTIONS: BIG DATA MININGcscpconf
Data has become an indispensable part of every economy, industry, organization, business
function and individual. Big Data is a term used to identify the datasets that whose size is
beyond the ability of typical database software tools to store, manage and analyze. The Big
Data introduce unique computational and statistical challenges, including scalability and
storage bottleneck, noise accumulation, spurious correlation and measurement errors. These
challenges are distinguished and require new computational and statistical paradigm. This
paper presents the literature review about the Big data Mining and the issues and challenges
with emphasis on the distinguished features of Big Data. It also discusses some methods to deal
with big data.
Data has become an indispensable part of every economy, industry, organization, business
function and individual. Big Data is a term used to identify the datasets that whose size is
beyond the ability of typical database software tools to store, manage and analyze. The Big
Data introduce unique computational and statistical challenges, including scalability and
storage bottleneck, noise accumulation, spurious correlation and measurement errors. These
challenges are distinguished and require new computational and statistical paradigm. This
paper presents the literature review about the Big data Mining and the issues and challenges
with emphasis on the distinguished features of Big Data. It also discusses some methods to deal
with big data.
This document presents a proposed system for big data processing and data mining. It introduces the HACE theorem to characterize big data using the characteristics of being huge, autonomous, complex, and evolving. The proposed system advocates for a stream-based analytic framework to enable fast response and real-time decision making on big data. It also describes modules for integrating and mining biodata, pattern matching and mining, key technologies for integration, and analyzing group influence and interactions on social networks.
The document discusses how to gain understanding from big data through effective data governance and classification. It argues that proper categorization of data using controlled vocabularies, taxonomies, and ontologies improves search, analytics and other uses of big data. A framework is presented outlining the key components of a data governance lifecycle for big data, including content creation, mining and classification, management of vocabularies/taxonomies/ontologies, and use of the structured data for search, transactions and analytics. Effective use of this framework can help organizations apply meaning and understanding to their big data.
Mining Big Data using Genetic AlgorithmIRJET Journal
This document discusses using genetic algorithms to mine big data through clustering. It begins by introducing big data and the challenges of analyzing large and complex data sets using traditional methods. It then proposes using a combination of genetic algorithms and existing clustering algorithms to more efficiently process big data. Specifically, it suggests genetic algorithms can optimize clustering results for big data by combining advantages of genetic algorithms and clustering. The document provides an overview of concepts like data mining, genetic algorithms and big data, and how genetic algorithms may be applied to clustering large data sets.
A Survey On Ontology Agent Based Distributed Data MiningEditor IJMTER
With the increased complexity in number of applications and due to large volume
of availability of data from heterogeneous sources, there is a need for the development of
suitable ontology, which can handle the large data set and present the mined outcomes for
evaluation intelligently. In the era of intensive data driven applications distributed data mining can
meet the challenges with the support of agents. This paper discusses the underlying principles for
effectiveness of modern agent-based systems for distributed data mining
This document defines big data and discusses its key characteristics and applications. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data that is difficult to process using traditional methods. It then outlines the 5 Vs of big data: volume, velocity, variety, veracity, and variability. The document also discusses Hadoop as an open-source framework for distributed storage and processing of big data, and lists several applications of big data across various industries. Finally, it discusses both the risks and benefits of working with big data.
This document provides an overview of big data, including definitions, characteristics, and technologies. It defines big data as large datasets that cannot be processed by traditional databases due to size and complexity. It describes the key aspects of big data as volume, variety, velocity, and veracity. The document also discusses how big data differs from traditional transaction systems, the promise and challenges of big data, and Hadoop as a framework for distributed processing of big data.
1) The document discusses using k-means clustering to analyze big data. K-means is an algorithm that partitions data into k clusters based on similarity.
2) It provides background on big data characteristics like volume, variety, and velocity. It also discusses challenges of heterogeneous, decentralized, and evolving data.
3) The document proposes applying k-means clustering to big data to map data into clusters according to its properties in a fast and efficient manner. This allows statistical analysis and knowledge extraction from large, complex datasets.
This document summarizes a research paper on using k-means clustering to analyze big data. It begins with an introduction to big data and its characteristics. It then discusses related work on big data storage, mining, and analytics. The HACE theorem for defining big data is presented. The k-means clustering algorithm is explained as an efficient method for partitioning big data into groups. The proposed system uses k-means clustering followed by data mining and classification modules. Experimental results on two datasets show that the recursive k-means approach finds clusters closer to the actual number than the iterative approach. In conclusion, clustering is effective for handling big data attributes like heterogeneity and complexity, and k-means distribution helps distribute data into appropriate clusters.
This document discusses the need for a new paradigm in big data analytics using algorithms. It begins by describing the limitations of traditional analytics approaches like statistical analysis, data mining, visualization and business intelligence tools when applied to big data. These approaches are query-based and labor intensive. Emerging big data tools like Hadoop and in-memory databases help with storage and queries but do not provide automated insights. The document argues that the new paradigm should focus on algorithms that can automatically surface insights from data in seconds, replacing the need for data analysts to manually query databases. This represents a shift from humans digging for insights to algorithms surfacing insights for humans to evaluate.
This document provides an overview of big data, including its definition, size and growth, characteristics, analytics uses and challenges. It discusses operational vs analytical big data systems and technologies like NoSQL databases, Hadoop and MapReduce. Considerations for selecting big data technologies include whether they support online vs offline use cases, licensing models, community support, developer appeal, and enabling agility.
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Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
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Data Mining with big data total ieee project and entire files.
1. ABSTRACT
Data mining Revolution on High Dimensional Data
Data mining involves exploring and analyzing large amounts of data to find patterns for big data.
Data volumes grow exponentially. Its growth is caused by the increasing number of systems
and people acting as data sources of textual, verbal, video and transactional information. This
data contains insider information and patterns previously hidden due to lack of proper
technologies. Generally, the goal of the data mining is either classification or prediction. In
classification, the idea is to sort data into groups. For example, a marketer might be interested in
the characteristics of those who responded versus who didn’t respond to a promotion. Big Data
concern large-volume, complex, growing data sets with multiple, autonomous sources. This
paper presents a HACE theorem that characterizes the features of the Big Data revolution, and
proposes a Big Data processing model, from the data mining perspective. Our HACE theorem
suggests that the key characteristics of the Big Data are 1) huge with heterogeneous and diverse
data sources, 2) Autonomous with distributed and decentralized control, and 3) complex and
evolving in data and knowledge associations.