This document summarizes a review of 30 research papers on data security primitives in data mining. The review identified 9 key issues: spatial data handling, gaps between hidden patterns and business tools, decision making in heterogeneous databases, resource mining, visually interactive data mining, data cluster mining, load balancing and data fittability, privacy preservation, and mining complex patterns. For each issue, the document discusses solution approaches from the papers and identifies the best and worst approaches. Common findings are presented across the issues. The document concludes there is scope for future work integrating optimization techniques with neural networks for improved data mining and increasing system flexibility.
Data mining involves analyzing large datasets to discover patterns using techniques from machine learning, statistics, and database systems. It is used to extract useful information from large datasets and predict future outcomes. The goal is often predictive analysis to forecast behaviors. The data mining process involves data preparation, model building and validation, and model deployment. Common tools for data mining include neural networks, decision trees, rule induction, genetic algorithms, and nearest neighbor algorithms. While data mining provides benefits like improved marketing and fraud detection, it also raises privacy and security issues regarding personal information.
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.
What is Data mining? Data mining Presentation Pralhad Rijal
Data mining involves discovering knowledge from large amounts of data through processes like extraction, cleansing, integration, transformation, and analysis. It aims to extract useful information for purposes like market analysis, risk analysis, detecting patterns, and improving websites. Key techniques include association rule mining to analyze customer purchasing patterns, classification to categorize and predict items, and clustering to group similar objects together.
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 summarizes a seminar presentation on using data mining techniques for telecommunications. It discusses three main types of telecom data: call summary data, network data, and customer data. It then describes using a genetic algorithm approach to mine sequential patterns from telecom databases. The genetic algorithm uses country codes to represent chromosomes and applies genetic operators and fitness functions to iteratively find sequential patterns in the telecom data. The approach provides non-optimal solutions faster than traditional algorithms.
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.
Different Classification Technique for Data mining in Insurance Industry usin...IOSRjournaljce
this paper addresses the issues and techniques for Property/Casualty actuaries applying data mining methods. Data mining means the effective unknown pattern discovery from a large amount database. It is an interactive knowledge discovery procedure which is includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the data discovery method and introduces some important data mining method for application to insurance concluding cluster discovery approaches.
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.
Data mining involves analyzing large datasets to discover patterns using techniques from machine learning, statistics, and database systems. It is used to extract useful information from large datasets and predict future outcomes. The goal is often predictive analysis to forecast behaviors. The data mining process involves data preparation, model building and validation, and model deployment. Common tools for data mining include neural networks, decision trees, rule induction, genetic algorithms, and nearest neighbor algorithms. While data mining provides benefits like improved marketing and fraud detection, it also raises privacy and security issues regarding personal information.
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.
What is Data mining? Data mining Presentation Pralhad Rijal
Data mining involves discovering knowledge from large amounts of data through processes like extraction, cleansing, integration, transformation, and analysis. It aims to extract useful information for purposes like market analysis, risk analysis, detecting patterns, and improving websites. Key techniques include association rule mining to analyze customer purchasing patterns, classification to categorize and predict items, and clustering to group similar objects together.
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 summarizes a seminar presentation on using data mining techniques for telecommunications. It discusses three main types of telecom data: call summary data, network data, and customer data. It then describes using a genetic algorithm approach to mine sequential patterns from telecom databases. The genetic algorithm uses country codes to represent chromosomes and applies genetic operators and fitness functions to iteratively find sequential patterns in the telecom data. The approach provides non-optimal solutions faster than traditional algorithms.
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.
Different Classification Technique for Data mining in Insurance Industry usin...IOSRjournaljce
this paper addresses the issues and techniques for Property/Casualty actuaries applying data mining methods. Data mining means the effective unknown pattern discovery from a large amount database. It is an interactive knowledge discovery procedure which is includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the data discovery method and introduces some important data mining method for application to insurance concluding cluster discovery approaches.
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.
Shivani Soni presented on data mining. Data mining involves using computational methods to discover patterns in large datasets, combining techniques from machine learning, statistics, artificial intelligence, and database systems. It is used to extract useful information from data and transform it into an understandable structure. Data mining has various applications, including in sales/marketing, banking/finance, healthcare/insurance, transportation, medicine, education, manufacturing, and research analysis. It enables businesses to understand customer purchasing patterns and maximize profits. Examples of its use include fraud detection, credit risk analysis, stock trading, customer loyalty analysis, distribution scheduling, claims analysis, risk profiling, detecting medical therapy patterns, education decision making, and aiding manufacturing process design and research.
Data mining is the process of discovering useful patterns from large amounts of data using statistical, mathematical, and artificial intelligence techniques. It involves applying these techniques to extract and identify useful information from large datasets. Data mining draws from multiple disciplines including statistics, pattern recognition, mathematical modeling, information systems, and machine learning. It has various applications in domains such as customer relationship management, banking, retailing, manufacturing, insurance, software, government, travel, and healthcare. The CRISP-DM process provides a standard methodology for data mining projects involving six steps: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
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.
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
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.
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.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Data mining involves using computer algorithms to analyze large datasets and infer new information. It has traditionally been done by data analysts but computers now allow for more efficient analysis. Data mining has two main components - knowledge discovery from known data and knowledge prediction using data to forecast trends. It uses techniques like decision trees and clustering. While valuable, data mining raises privacy concerns as personal data is increasingly mined without consent.
The document discusses the process of knowledge discovery in databases (KDP). It provides the following key points:
1. KDP involves discovering useful information from data through steps like data cleaning, transformation, mining and pattern evaluation.
2. Several KDP models have been developed, including academic models with 9 steps, industrial models with 5-6 steps, and hybrid models combining aspects of both.
3. A widely used model is CRISP-DM, which stands for Cross-Industry Standard Process for Data Mining and has 6 steps: business understanding, data understanding, data preparation, modeling, evaluation and deployment.
DATA MINING AND DATA WAREHOUSE
W.H. Inmon
OLAP, (On-line analytical processing)
OLTP, ( On-line transaction processing )
Data Cleaning
Data Integration
Data Selection
Data Transformation
Data warehouse vs Data Mining
Use in Urban Planning
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
- Data mining involves discovering novel patterns from large databases using algorithms and computers. It aims to find hidden patterns in datasets by analyzing attribute correlations.
- Common data mining tasks include classification, regression, clustering, association analysis, and anomaly detection. These can be used to solve problems like product recommendations, student enrollment predictions, and fraud detection.
- The key steps in data mining typically involve data preparation, exploration, model development, and result interpretation. Association rule mining is commonly used and aims to find relationships between variables in large datasets.
Data mining refers to extracting hidden patterns from large databases and is a step in the Knowledge Discovery in Databases (KDD) process. KDD is the broader process of finding knowledge within data and involves data preparation, pattern analysis, and knowledge evaluation. It is needed due to the impracticality of manually analyzing large, complex databases. The KDD process includes understanding goals, data selection, preprocessing, mining, pattern recognition, interpretation, and discovery. Examples of applying KDD include grouping students, predicting enrollments, and assessing student performance.
Study of Data Mining Methods and its ApplicationsIRJET Journal
This document discusses data mining methods and their applications. It begins by defining data mining as the process of extracting useful patterns from large amounts of data. The document then outlines the typical steps in the knowledge discovery process, including data selection, preprocessing, transformation, mining, and evaluation. It classifies data mining techniques into predictive and descriptive methods. Specific techniques discussed include classification, clustering, prediction, and association rule mining. Finally, the document discusses applications of data mining in fields like healthcare, biology, retail, and banking.
The document discusses data mining and knowledge discovery in databases (KDD). It defines data mining and describes some common data mining tasks like classification, regression, clustering, and summarization. It also explains the KDD process which involves data selection, preprocessing, transformation, mining and interpretation. Data preprocessing tasks like data cleaning, integration and reduction are discussed. Methods for handling missing, noisy and inconsistent data are also covered.
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.
Applying K-Means Clustering Algorithm to Discover Knowledge from Insurance Da...theijes
Data mining works to extract information known in advance from the enormous quantities of data which can lead to knowledge. It provides information that helps to make good decisions. The effectiveness of data mining in access to knowledge to achieve the goal of which is the discovery of the hidden facts contained in databases and through the use of multiple technologies. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. This paper deals with K-means clustering algorithm which collect a number of data based on the characteristics and attributes of this data, and process the Clustering by reducing the distances between the data center. This algorithm is applied using open source tool called WEKA, with the Insurance dataset as its input
1) The survey analyzed 95 unemployed individuals in Romania to understand their experiences, attitudes, and needs. Most respondents were between 30-49 years old and had lower levels of education.
2) Long-term unemployment (>6 months) was associated with increased feelings of helplessness, depression, and uncertainty about finding adequate work. Older respondents seemed particularly impacted.
3) Respondents had mixed views of employment offices - some felt clerks tried to help but were limited, while others felt support was inadequate and clerks did not understand their situations well. Effective communication between clerks and unemployed individuals seems important to providing meaningful assistance.
The document is a curriculum vitae for an individual named KIRUPAKARAN.S with 1 year of experience developing Java and Android applications. They have skills in languages like Java, C++, and Android development tools. They have a Master's degree in Computer Applications and have worked on projects like VoiceMailPro and Court Diary apps at Qmax Systems India Pvt Ltd.
Pranešimas XVII mokslinės kompiuterininkų konferencijos
sekcijoje „K8. Statistiniai metodai, optimizavimas ir prognozavimas“
„Kompiuterininkų dienos – 2015“, Panevėžyje, KTU PTVF 2013-09-19
Shivani Soni presented on data mining. Data mining involves using computational methods to discover patterns in large datasets, combining techniques from machine learning, statistics, artificial intelligence, and database systems. It is used to extract useful information from data and transform it into an understandable structure. Data mining has various applications, including in sales/marketing, banking/finance, healthcare/insurance, transportation, medicine, education, manufacturing, and research analysis. It enables businesses to understand customer purchasing patterns and maximize profits. Examples of its use include fraud detection, credit risk analysis, stock trading, customer loyalty analysis, distribution scheduling, claims analysis, risk profiling, detecting medical therapy patterns, education decision making, and aiding manufacturing process design and research.
Data mining is the process of discovering useful patterns from large amounts of data using statistical, mathematical, and artificial intelligence techniques. It involves applying these techniques to extract and identify useful information from large datasets. Data mining draws from multiple disciplines including statistics, pattern recognition, mathematical modeling, information systems, and machine learning. It has various applications in domains such as customer relationship management, banking, retailing, manufacturing, insurance, software, government, travel, and healthcare. The CRISP-DM process provides a standard methodology for data mining projects involving six steps: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
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.
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
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.
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.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
Data mining involves using computer algorithms to analyze large datasets and infer new information. It has traditionally been done by data analysts but computers now allow for more efficient analysis. Data mining has two main components - knowledge discovery from known data and knowledge prediction using data to forecast trends. It uses techniques like decision trees and clustering. While valuable, data mining raises privacy concerns as personal data is increasingly mined without consent.
The document discusses the process of knowledge discovery in databases (KDP). It provides the following key points:
1. KDP involves discovering useful information from data through steps like data cleaning, transformation, mining and pattern evaluation.
2. Several KDP models have been developed, including academic models with 9 steps, industrial models with 5-6 steps, and hybrid models combining aspects of both.
3. A widely used model is CRISP-DM, which stands for Cross-Industry Standard Process for Data Mining and has 6 steps: business understanding, data understanding, data preparation, modeling, evaluation and deployment.
DATA MINING AND DATA WAREHOUSE
W.H. Inmon
OLAP, (On-line analytical processing)
OLTP, ( On-line transaction processing )
Data Cleaning
Data Integration
Data Selection
Data Transformation
Data warehouse vs Data Mining
Use in Urban Planning
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
- Data mining involves discovering novel patterns from large databases using algorithms and computers. It aims to find hidden patterns in datasets by analyzing attribute correlations.
- Common data mining tasks include classification, regression, clustering, association analysis, and anomaly detection. These can be used to solve problems like product recommendations, student enrollment predictions, and fraud detection.
- The key steps in data mining typically involve data preparation, exploration, model development, and result interpretation. Association rule mining is commonly used and aims to find relationships between variables in large datasets.
Data mining refers to extracting hidden patterns from large databases and is a step in the Knowledge Discovery in Databases (KDD) process. KDD is the broader process of finding knowledge within data and involves data preparation, pattern analysis, and knowledge evaluation. It is needed due to the impracticality of manually analyzing large, complex databases. The KDD process includes understanding goals, data selection, preprocessing, mining, pattern recognition, interpretation, and discovery. Examples of applying KDD include grouping students, predicting enrollments, and assessing student performance.
Study of Data Mining Methods and its ApplicationsIRJET Journal
This document discusses data mining methods and their applications. It begins by defining data mining as the process of extracting useful patterns from large amounts of data. The document then outlines the typical steps in the knowledge discovery process, including data selection, preprocessing, transformation, mining, and evaluation. It classifies data mining techniques into predictive and descriptive methods. Specific techniques discussed include classification, clustering, prediction, and association rule mining. Finally, the document discusses applications of data mining in fields like healthcare, biology, retail, and banking.
The document discusses data mining and knowledge discovery in databases (KDD). It defines data mining and describes some common data mining tasks like classification, regression, clustering, and summarization. It also explains the KDD process which involves data selection, preprocessing, transformation, mining and interpretation. Data preprocessing tasks like data cleaning, integration and reduction are discussed. Methods for handling missing, noisy and inconsistent data are also covered.
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.
Applying K-Means Clustering Algorithm to Discover Knowledge from Insurance Da...theijes
Data mining works to extract information known in advance from the enormous quantities of data which can lead to knowledge. It provides information that helps to make good decisions. The effectiveness of data mining in access to knowledge to achieve the goal of which is the discovery of the hidden facts contained in databases and through the use of multiple technologies. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. This paper deals with K-means clustering algorithm which collect a number of data based on the characteristics and attributes of this data, and process the Clustering by reducing the distances between the data center. This algorithm is applied using open source tool called WEKA, with the Insurance dataset as its input
1) The survey analyzed 95 unemployed individuals in Romania to understand their experiences, attitudes, and needs. Most respondents were between 30-49 years old and had lower levels of education.
2) Long-term unemployment (>6 months) was associated with increased feelings of helplessness, depression, and uncertainty about finding adequate work. Older respondents seemed particularly impacted.
3) Respondents had mixed views of employment offices - some felt clerks tried to help but were limited, while others felt support was inadequate and clerks did not understand their situations well. Effective communication between clerks and unemployed individuals seems important to providing meaningful assistance.
The document is a curriculum vitae for an individual named KIRUPAKARAN.S with 1 year of experience developing Java and Android applications. They have skills in languages like Java, C++, and Android development tools. They have a Master's degree in Computer Applications and have worked on projects like VoiceMailPro and Court Diary apps at Qmax Systems India Pvt Ltd.
Pranešimas XVII mokslinės kompiuterininkų konferencijos
sekcijoje „K8. Statistiniai metodai, optimizavimas ir prognozavimas“
„Kompiuterininkų dienos – 2015“, Panevėžyje, KTU PTVF 2013-09-19
This document introduces the candidate sourcing company vsource. It describes how vsource uses technology like resume parsing, boolean logic, and data science to build pipelines of qualified candidates for its clients. This allows recruiters to maximize their time focusing on outreach and assessments rather than manual research. The document outlines vsource's 100 day roadmap pilot program where they work with a client to understand needs, provide access to their portal and sourcing expertise, conduct reviews and reports to optimize results. It provides examples of positive feedback from demanding clients who value vsource's integration, efficiency and solutions.
This document discusses the potential benefits and aspects to consider regarding m-learning or mobile learning. It describes m-learning as a new educational awakening and discusses using mobile devices in the classroom as a tool for collaborative work rather than just recreational use. It emphasizes the importance of teacher training, adapting to student needs, creating educational policies, teaching values, motivating students, meaningful learning, diversity of educational contexts, and ensuring mobile devices supplement rather than replace other tools. The document advocates for the good use of mobile devices as another educational tool and the need for educators to adapt to the changing needs of students in a constantly changing world.
This document provides information about the website www.shurukaro.com. It is a community portal that aims to inspire people by sharing stories of ordinary people who have achieved extraordinary things. Users can create groups, join groups, and discuss inspiring quotes. The site features interviews with successful people who share advice. It also discusses initiatives like a virtual film festival and paying medicine costs for those who cannot afford it. The goal is to stir people's conscience and encourage them to help others or share useful information.
The document discusses three potential locations for shooting advertising videos:
1) A quiet street in Nottingham known for its architecture would be used for an daytime shoot, providing props like a phone booth.
2) A photography studio in Hinckley owned by a friend's sister would be ideal for a night-themed shoot, as its white backdrop would make clothing stand out under studio lighting.
3) A model's bedroom with natural light and space has been selected for a sponsorship sequence, featuring fashion magazines and basic colors, focusing the audience on the model and clothing while its connotations of sexuality link to ideologies about women's fashion.
This document outlines We Are Aggie Pride's (WAAP) framework for planning and executing successful campus-wide events like their annual Stride for Aggie Pride 5K fundraiser. It discusses generating campus support by filling a need, developing a core team, and managing partnerships. It also covers building effective sponsorship programs by identifying stakeholders, evaluating sponsors, and maintaining accountability. Finally, it provides tips for creating an event plan such as choosing a date, developing a budget and marketing strategy, and creating a timeline from one year to days before the event. WAAP has grown their 5K event from 500 participants raising $5,000 in 2013 to 3,500 participants raising almost $25,000 in 2015 using this framework.
The document discusses how technology has changed how people store and recall information. It summarizes research finding that when people are told information will be saved digitally, they are worse at remembering it, as they rely on the device to store it. Additionally, people are better at recalling where information is located digitally rather than the actual content. This suggests people prioritize location of information over details, and digital devices are becoming like a shared external memory for information.
The document discusses the real estate market in India. It defines real estate and outlines its importance to the Indian economy, noting that it is the second largest employer and housing contributes 5-6% to GDP. Growth in the market has been driven by factors like nuclear families, urbanization, and rising incomes. Real estate developers play a key role by bridging construction capabilities and customer needs. The document also outlines the steps to build a real estate brand, including situation analysis, creating value through segmentation and marketing mix, capturing value through pricing strategies, and sustaining value through customer acquisition and retention. It concludes by discussing various sales channels and key risks and concerns in the industry.
This document summarizes the layout and design of 4 different magazine contents pages:
1. The first uses a bold masthead at the top right, a full-body image of a woman in the center, and small snippets of text with page numbers and subtitles.
2. The second has a small masthead in the top right corner, thumbnail images throughout, and bold subtitles against colored backgrounds to highlight sections.
3. The third contains no masthead but provides page references. It features a central image of Eminem in a dark suit with an angry expression. Text is in a basic white font with subheadings and page numbers.
4. The fourth has a dark red background with light colors
This document provides instructions for making a homemade tuna sandwich. It begins by listing the main ingredients needed: white bread, tuna, mayonnaise, salt or chicken powder, tomato, cucumber, and onion. It then outlines a 3 step process: 1) slicing the bread diagonally, 2) slicing the vegetables into circles, and 3) making the tuna paste by mixing tuna, mayonnaise, and optional seasonings like sesame oil and pepper. Finally, it describes assembling the sandwich by layering the ingredients between the bread slices and enjoying the easy to make, hygienic sandwich.
This document provides instructions for analyzing APK files using various tools, including apktool to decompile and recompile APKs, signapk to sign APKs, IDA for static analysis, and memdump to dump process memory for dynamic analysis. It also notes that the next steps would be to analyze the smali code and perform native library analysis and ARM assembly code.
The AHEAD project aims to produce ICT tools for seniors to share their travel experiences. It will create a mobile app and platform for seniors to store and organize photos, videos, and audio from their travels into stories or diaries. The tools will allow seniors to promote active aging through sharing their travel experiences and equip them with digital skills to use their stories for innovative learning for young people. The key aspects of the project are the travel content created by seniors and the sharing tools to disseminate the content. The platform located at aheadapp.net allows users to register, create blogs and video stories from their travels, and explore content shared by other users.
This short document contains 6 photo credits attributed to different photographers and suggests that the reader may be inspired to create their own presentation on SlideShare using Haiku Deck. It closes by providing a call to action to "GET STARTED" making their own presentation.
This short document promotes creating presentations using Haiku Deck, a tool for making slideshows. It encourages the reader to get started making their own Haiku Deck presentation and sharing it on SlideShare. In a single sentence, it pitches the idea of using Haiku Deck to easily create and publish online presentations.
Fundamentals of data mining and its applicationsSubrat Swain
Data mining involves applying intelligent methods to extract patterns from large data sets. It is used to discover useful knowledge from a variety of data sources. The overall goal is to extract human-understandable knowledge that can be used for decision-making.
The document discusses the data mining process, which typically involves problem definition, data exploration, data preparation, modeling, evaluation, and deployment. It also covers data mining software tools and techniques for ensuring privacy, such as randomization and k-anonymity. Finally, it outlines several applications of data mining in fields like industry, science, music, and more.
IRJET- Fault Detection and Prediction of Failure using Vibration AnalysisIRJET Journal
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Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
This document provides an overview of knowledge discovery and data mining in databases. It discusses how knowledge discovery in databases is the process of finding useful knowledge from large datasets, with data mining being the core step that extracts patterns from data. The document outlines the common steps in the knowledge discovery process, including data preparation, data mining algorithm selection and employment, pattern evaluation, and incorporating discovered knowledge. It also describes different data mining techniques such as prediction, classification, and clustering and their goals of extracting meaningful information from data.
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
Data mining involves analyzing large datasets to discover patterns and extract useful information. It has evolved from early methods like regression analysis and involves techniques from machine learning, statistics, and databases. Data mining is used for applications like market analysis, fraud detection, customer retention, and science exploration by performing descriptive tasks like frequent pattern mining and associations or classification/prediction tasks. It involves preprocessing data, extracting patterns, and evaluating and presenting results.
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International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
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Data mining techniques are used to analyze large datasets and discover hidden patterns. There are three main types of data mining techniques: supervised, unsupervised, and semi-supervised learning. Supervised learning uses labeled training data to learn relationships between inputs and outputs. Unsupervised learning looks for patterns in unlabeled data. Semi-supervised learning uses some labeled and mostly unlabeled data. The knowledge discovery in databases (KDD) process is a nine step method for applying data mining techniques which includes data selection, preprocessing, transformation, mining, and interpretation.
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Data mining involves analyzing large amounts of data to discover patterns that can be used for purposes such as increasing sales, reducing costs, or detecting fraud. It allows companies to better understand customer behavior and develop more effective marketing strategies. Common data mining techniques used by retailers include loyalty programs to track purchasing patterns and target customers with personalized coupons. Data mining software uses techniques like classification, clustering, and prediction to analyze data from different perspectives and extract useful information and patterns.
This document summarizes a survey on data mining. It discusses how data mining helps extract useful business information from large databases and build predictive models. Commonly used data mining techniques are discussed, including artificial neural networks, decision trees, genetic algorithms, and nearest neighbor methods. An ideal data mining architecture is proposed that fully integrates data mining tools with a data warehouse and OLAP server. Examples of profitable data mining applications are provided in industries such as pharmaceuticals, credit cards, transportation, and consumer goods. The document concludes that while data mining is still developing, it has wide applications across domains to leverage knowledge in data warehouses and improve customer relationships.
This document discusses data mining and provides an overview of the topic. It begins by defining data mining as the process of analyzing large amounts of data to discover hidden patterns and rules. The goal is to analyze this data and summarize it into useful information that can be used to make decisions.
It then describes some common data mining techniques like decision trees, neural networks, and clustering. It also discusses the typical stages of a data mining project, including business understanding, data preparation, modeling, evaluation, and deployment.
Finally, it provides examples of applications for data mining, such as in healthcare to identify patterns in patient data, education to improve learning outcomes, and manufacturing to enhance product quality. In summary, the document outlines the
Introduction to feature subset selection methodIJSRD
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
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Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
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6 ijaems sept-2015-6-a review of data security primitives in data mining
1. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-1, Issue-6, Sept- 2015]
ISSN : 2454-1311
www.ijaems.com Page | 25
A Review of Data Security Primitives in Data
Mining
Asmita Singh, Anchal Pokharana
Department of CE, Poornima University, Jaipur, Rajasthan, India
Abstract—This paper has discussed various issues and
security primitives like Spatial Data Handing, Privacy
Protection of data, Data Load Balancing, Resource Mining
etc. in the area of Data Mining.A 5-stage review process has
been conductedfor 30 research papers which were published
in the period of year ranging from 1996 to year 2013. After
an exhaustive review process, nine key issues were found
“Spatial Data Handing, Data Load Balancing, Resource
Mining ,Visual Data Mining, Data Clusters Mining, Privacy
Preservation, Mining of gaps between business tools &
patterns, Mining of hidden complex patterns.” which have
been resolved and explained with proper methodologies.
Several solution approaches have been discussed in the 30
papers. This paper provides an outcome of the review which
is in the form of various findings, found under various key
issues. The findings included algorithms and methodologies
used by researchers along with their strengths and
weaknesses and the scope for the future work in the area.
Keywords—Data load balancing , Privacy , D3M , AKD ,
Data Hiding.
I. INTRODUCTION
To generate information it requires massive collection of
data. The data can be simple numerical figures and text
documents, to more complex information such as spatial data,
multimedia data and hypertext documents. With enormous
amount of data stored in files, database and other repositories,
it is increasingly important, to develop powerful tool for
analysis and interpretation of such data and for the extraction
of interesting knowledge that could help in Decision making.
Data mining is a set of activities used to find new, hidden or
unexpected patterns in data or unusual patterns in data. Data
mining tools predict future trends and behaviors,allowing
businesses to make proactive, knowledge-drivendecisions.
The automated, prospective analyses offered bydata mining
move beyond the analyses of past events provided by
retrospective tools typical of decision supportsystems. Data
mining tools can answer business questionsthat traditionally
were too time consuming to resolve.They search databases
for hidden patterns, finding predictive information that
experts may miss because it lies outside their expectations.
Different types of data mining tools are available in the
marketplace, each with their own strengths and weaknesses.
Internal auditors need to be aware of the different kinds of
data mining tools available and recommend the purchase of a
tool that matches the organization's current detective needs.
Data mining commonly involves four classes of tasks[7].
Classification - Arranges the data into predefined groups
.For example an email program might attempt to classify an
email as legitimate or spam. Common algorithms include
Decision Tree Learning, Nearest neighbour, naive Bayesian
classification and Neural network[2].
Clustering - Is like classification but the groups are not
predefined, so the algorithm will try to group similar items
together[2].
Regression - Attempts to find a function which models the
data with the least error[2].
Association rule learning - Searches for relationships
between variables. For example a supermarket might gather
data on customer purchasing habits. Using association rule
learning, the supermarket can determine which products are
frequently bought together and use this information for
marketing purposes. This is sometimes referred to as "market
basket analysis"[2].
II. REVIEW PROCESS ADOPTED
This review process approach was divided into five stages in
order to make the process simple and adaptable. The stages
were:-
Fig1: Review Process Adopted
Stage 0: Get a “feel”:
2. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-1, Issue-6, Sept- 2015]
ISSN : 2454-1311
www.ijaems.com Page | 26
This stage provides the details to be checked while starting
literature survey with a broader domain and classifying them
according to requirements.
Stage 1: Get the “big picture”
The groups of research papers are prepared according to
common issues & application sub areas. In order to
understand the paper, it is necessary to find out the answers
to certain questions by reading the Title, Abstract,
introduction, conclusion and section and sub section
headings.
Stage 2: Get the “details”
Stage 2 deals with going in depth of each research paper and
understand the details of methodology used to justify the
problem, justification to significance & novelty of the
solution approach, precise question addressed, major
contribution, scope & limitations of the work presented.
Stage 3: “Evaluate the details”
This stage evaluates the details in relation to significance of
the problem, Novelty of the problem, significance of the
solution, novelty in approach, validity of claims etc.
Stage 3+: “Synthesize the detail”
Stage 3+ deals with evaluation of the details presented and
generalization to some extent. This stage deals with
synthesis of the data, concept & the results presented by the
authors
III. VARIOUS ISSUES IN THE AREA
After reviewing 30 research papers on Data Mining we have
found following issues, which have been listed as under. The
issues are:
1) Spatial Data Handling and Mining
2) Gap between various hidden patterns and
business tools
3) Problem of decision making in heterogeneous
data bases
4) Problem of Resource Mining
5) Problem of mining of visually interactive data
6) Problem of mining of data clusters
7) Mining of data in terms of load balancing and
data fittability
8) Problem of protecting and preserving data
9) Mining of various complex and hidden patterns
of data
IV. ISSUE WISE DISCUSSION
Issue 1:- Spatial Data Handling and Mining.
Some approaches were used for this issue which is spatial
clustering,spatial classification, spatial characterization and
spatio-temporal association rule mining are performed for
spatial data mining. Data model of the spatial data cube has
also been proposed..The selection in spatial data cube
performed at cuboids level. For better sup By these solution
approaches , spatial data can be properly handled and mined.
Issue 2:- Gap between various hidden patterns and
business tools.
Task driven data mining, domain driven data mining (D3M)
and actionable knowledge discovery (AKD) database are the
approaches that have been given . Task driven data mining
system involves seven elements such as data warehousing,
data pre -processing, feature subset selecting, modelling,
model evaluating, model updating and model releasing
D3M solves the developing problem of areas by many
intelligence methods. It ensures decision making at the same
time from the different fields
Issue 3:- Problem of decision making in heterogeneous
data bases.
The technique of “Intelligent data mining system” for bio
database solves the problem of evaluation and analysis of bio
data and decision making process. It collects data from
distributed data bases and provides integrated data, which is
used with other data for analysis purpose then extract valid,
relevant information from bio databases.
Issue 4:- Problem of Resource Mining.
Model driven data mining effectively mines the categories of
data in oil and gas exploration and production. Various
methodologies like model driven data mining,Intrusion
Detection, Predictive Data Mining, Descriptive Data Mining,
Clustering, E-commerce, Web mining and Business
Intelligence perfectly explain and mine the resources
efficiently.
Issue 5:- Problem of mining of visually interactive data.
A mechanism of bootstrapping data mining with
visualization has been provided. A smooth interface between
visualization and data mining is built & a flexible tool to
explore and query temporal data derived from raw
multimedia data.
Issue 6:- Problem of mining of data clusters.
A data clustering method named BIRCH has been
demonstrated which is highly efficient for clustering large
sized data bases. Another approach i.e. the CLARANS
algorithm is used to cluster the set of compound objects.
This algorithm is relational in the sense that it takes
relational data as input and does the proper mining of data
clusters.
Issue 7:- Mining of data in terms of load balancing and
data fittability.
Data type generalization process has been devised. Map ND
strategy is used for solving the problem. Parallel data mining
for data mapping has been used.Development of data mining
language,data mining query languages like DMQL and
TDML has been done for mining relational databases.
Issue 8:-Problem of preserving and protecting data.
Two approaches namely “perturbation approach” and “k-
anonymity” have been proposed. K-anonymity requires each
record in an anonymized table to be indistinguishable with at
least k-1 other records within the dataset. In perturbation
3. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-1, Issue-6, Sept- 2015]
ISSN : 2454-1311
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approach,distribution of each data dimension is
reconstructed independently. Two techniques namely
“Cryptographic techniques & Randomized Response
techniques” have also been proposed.
Issue 9:-Mining of various complex and hidden patterns
of data.
Artificial neural network system is devised to formulate such
problems. Artificial neural network provide robustness&data
parallelism in processing .Various neural network techniques
like maps, neuro fuzzy logic,adaptive resonance theory,
neuro-computing&natural intelligent systems have been
given.
V. ISSUE WISE SOLUTION APPROACHES USED
The solution approaches under the various issues have been
shown in the Table I to IX, which includes additional
information like hardware, software, variable/parameters
used along with results obtained. The same table also
describes the comparative analysis between various solution
approaches.
VI.ISSUE WISE DISCUSSION ON RESULTS
Table I Spatial Data Handling and Mining
Solution Approach Results Ref
Spatial
classification,
& Characterization,
Spatio-temporal
association- rule
mining.
Extracts spatial
patterns and
knowledge from a
spatial database.
[3]
Model of Spatial
data cube
Supports both
spatial and non
-spatial data and
mines data at global
level
[1]
Table II Gap between Various Hidden Patterns and Business
Tools
Solution Approach Results R
ef.
Domain Driven Data
Mining and
Actionable
Knowledge
Discovery Database .
D3M construct
next-generation
methodology which
solves the real world
problems.
[6
]
Task Driven Data
Mining
It rationally
combines domain
[8
]
and . knowledge with mining
methods.
Table III Problem of Decision Making in Heterogeneous
Data bases
Solution Approach Results Ref
Sensor based
intelligent mining &
Environmental
monitoring ontology
based reasoning
architecture
Early warning
systems which
helps in mining of
geological data
[12]
Knowledge
Discovery Database .
Evaluation and
analysis of valid ,
relevant
information from
bio databases.
[12]
Table IV Problem of Resource Mining
Solution Approach Results Ref
Model driven data
mining is used
Determines the
reliability and
practicality of
the mining
outcome
24
Table V Problem of Mining of Visually Interactive Data
Solution Approach Results Ref
A mechanism of
bootstrapping data &
smooth interface
between visualization
and data mining
Examine and
synthesize
information into
new ideas and
hypotheses
&test
the insights
gained from
visualization.
[15]
Table VI Problem of Mining of Data Clusters
Solution Approach Results Ref
Clustering algorithm
using IBM I-Miner has
been used
Consistency of
data is
maintained.
[4]
4. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-1, Issue-6, Sept- 2015]
ISSN : 2454-1311
www.ijaems.com Page | 28
BIRCH data clustering
method has been used.
Gives correct
output at the
first scan of data
[4]
Table VII Mining of Data in Terms of Load Balancing and
Data Fittability
Solution Approach Results Ref
MapND strategy
&Parallel data mining
Minimum time
cost has been
achieved..
[10]
Table VIII Problem of Protecting and Preserving Data
Solution Approach Results Ref
Bottom up
generalization
technique
,Cryptographic
technique &
Randomized response
techniques
Proper
Surveying of the
relationships
between data
forms, and then
analysis has
been done
[16]
Table XI Mining of Various Complex and Hidden Patterns of
Data
Solution Approach Results Ref
Artificial Neural
network techniques
like maps, neuro
fuzzy logic, parallel
distributed
processing, natural
intelligent systems
Good
robustness,
, adaptive
parallel
processing,
distributed
storage and high
degree of fault
tolerance
have been
achieved.
[18]
VII. COMMON FINDINGS
Issue 1:- Spatial Data Handling and Mining
The best solution Approach is ” Proposal of data model
of spatial data cube” because dimensions and measure
of the spatial data cube are extended to support both
spatial and non-spatial data.
The worst Approach is spatial classification and spatio-
temporal association mining because they require time
consuming computations and available analytical
operations are limited in them.
Issue 2:- Gap between various hidden patterns and
business tools
The best approach is Task driven data mining because
it is independent of type of data & is operational and
depends upon the tasks carried out on data.
The worst approach is of domain driven data mining
method. it is driven by the data & depends entirely on
the domain knowledge of extracted data.
Issue 3:- Problem of decision making in heterogeneous
data bases.
The best approach is Ontology based approach to
intelligent data mining for sensor networks because the
ability of the sensor networks to collect information
accurately enables building both real-time detection and
early warning systems.
Worst approach is Traditional data analysis techniques
because of insufficiency and could not support and
handle huge and complex biological data.
Issue 4:- Problem of Resource Mining.
In Fourth Issue the best approach is Model driven data
mining because effectively mines petro physical data,
geological data, seismic data and logging data by
mining actionable knowledge
The worst approach is Temporal association mining
Because they require time consuming computations and
available analytical operations which are lesser in
numbers.
Issue 5:- Problem of mining of visually interactive data
In Fifth Issue the best approach is Model driven data
mining because effectively mines petro physical data,
geological data, seismic data and logging data by
mining actionable knowledge
The worst approach is Temporal association mining
Because they require time consuming computations and
available analytical operations which are lesser in
numbers.
Issue 6:- Problem of mining of visually interactive data
In Sixth Issue the best approach is bootstrapping
mechanism because it allows users to easily examine
and synthesize & test the insights gained from
visualization.
The worst approach is Information visualization.
Because it involves problems like navigation between
spaces and transferability that need to be satisfied.
Issue 7:- Mining of data in terms of load balancing and
data fittability
In Seventh Issue the best approach is MapND strategy
because solve the problem of data load balancing for
data mining nodes, and improves the performance of
parallel data mining in grid and minimize the time cost.
The worst approach is Knowledge discovery database
technique Because the size of distributed database
increase & it results in inflexible results
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Issue 8:- Problem of protecting and preserving data
In Eighth Issue the best approach is bottom-up
generalization technique because incorporates partially
the requirement of a targeted data mining task into the
process of masking data so that essential structure is
preserved in the masked data
The worst approach is perturbation approach
Because it does not reconstruct the original data values
but only distributions, it is inefficient.
Issue 9:- Mining of various complex and hidden patterns
of data
In Ninth Issue the best approach is Artificial neural
network technique because mines the data with utmost
accuracy and will make the data noise tolerant
The worst approach is General framework Because it is
inefficient and often results in inconsistent mining of
data because it generally applied on uncertain data sets.
VIII. SCOPE OF WORK IN AREA
Particle swarm optimization, Ant colony
optimization can be integrated with artificial neural
network to further enhance the performance of
ANN in Data mining.
To increase the flexibility to be compatible with
data mining, our system allows users to use any
programming language to obtain new results. Thus,
data researchers can implement new data mining
algorithms using their own analysis tools (from
Matlab and to C/C++) as far as users write the
results into text files with pre-defined formats.
The insights from visualization can be used to guide
further data mining. Meanwhile, the results from
the next round of data mining can be visualized
which allows users to obtain new insights and
develop more hypotheses with the data.
IX. CONCLUSION
The review of 30 research papers has been carried out in the
area of Data Mining to investigate and find out current
challenges and scope of work. After the review, we found
several issues which should be given proper concern, when
the effective mining of data takes place. These papers are a
survey of different mining issues that affect the related work
that carried out in the area of data mining. Purpose of these
methods and techniques is to reduce the mining
inefficiencies that occurs while mining of data and to
improve system reliability. We have found various nine
issues for which specific methods and techniques have been
discussed.
The exhaustive review has finally led to extract findings in
the area of Data Mining, strengths and weaknesses and scope
of work during M. Tech 1st semester Research work.
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