Knowledge Discovery & Representation
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The document discusses knowledge discovery and data mining. It describes knowledge discovery as automatically searching large volumes of data for patterns that can be considered knowledge. The document outlines the five steps of the knowledge discovery process and notes it is closely related to data mining. It then discusses data mining, describing the purpose, preference, and search techniques used in data mining algorithms. The document also categorizes data mining and describes how it provides links between transactional and analytical systems to analyze relationships and patterns in stored data.
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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.
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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.
Data mining
The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
A SURVEY ON DATA MINING IN STEEL INDUSTRIES
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.
Data mining , Knowledge Discovery Process, Classification
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.
6 ijaems sept-2015-6-a review of data security primitives in data mining
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.doc
The document discusses data mining and knowledge discovery in databases. It defines data mining as the nontrivial extraction of implicit and potentially useful information from large amounts of data. With huge increases in data collection and storage, data mining aims to analyze data and discover patterns that can provide insights and knowledge about businesses and the real world. The data mining process involves selecting, preprocessing, transforming, and analyzing data to extract hidden patterns and relationships, which are then interpreted and evaluated.
data mining and data warehousing
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.
Data Mining Classification Comparison (Naïve Bayes and C4.5 Algorithms)
Data Mining Classification Comparison (Naïve Bayes and C4.5 Algorithms)Universitas Pembangunan Panca Budi
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.Dma unit 1
This document outlines the learning objectives and resources for a course on data mining and analytics. The course aims to:
1) Familiarize students with key concepts in data mining like association rule mining and classification algorithms.
2) Teach students to apply techniques like association rule mining, classification, cluster analysis, and outlier analysis.
3) Help students understand the importance of applying data mining concepts across different domains.
The primary textbook listed is "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber. Topics that will be covered include introduction to data mining, preprocessing, association rules, classification algorithms, cluster analysis, and applications.
International Journal of Engineering Research and Development (IJERD)
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Study and Analysis of K-Means Clustering Algorithm Using Rapidminer
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Data mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, Classification
6 ijaems sept-2015-6-a review of data security primitives in data mining
6 ijaems sept-2015-6-a review of data security primitives in data mining
Data Mining Classification Comparison (Naïve Bayes and C4.5 Algorithms)
Data Mining Classification Comparison (Naïve Bayes and C4.5 Algorithms)
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
Knowledge Discovery & Representation
- 3. 1.Introduction Knowledge discovery describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. It can be categorized according to 1) what kind of data is searched 2) in what form is the result of the search represented. Knowledge discovery developed out of the Data mining domain, and is closely related to it both in terms of methodology and terminology. Knowledge representation is a formalism for representing at least the data, information and knowledge things in an application. Knowledge can be represented either as programs in an imperative language or can be also represented as rules in a declarative language.
- 4. 2.Knowledge Discovery It is also known as Knowledge Discovery in Databases (KDD). Data Knowledge Discovery Process useful information Requires much elapsed time.
- 5. Five steps of KDD process
- 6. 3. Data Mining Data mining involves many different algorithms to accomplish different tasks Data mining algorithms can be characterized as consisting of three parts: • The purpose of algorithm is to fit a model to the data. Model • Some criteria must be used to fit one model over another. Preference • All algorithms require some technique to search the data. Search
- 7. 4. Classification of Data Mining
- 8. 5. Working of Data Mining Data mining provides link between separate transaction and analytical systems. Data mining software analyzes relationships and patterns in stored transaction data based on user queries. Generally four types of relationships are sought: classes, clusters, associations, sequential patters. Extract, transform, and load transaction data Present the data in a useful format Analyze the data by application software Store and manage the data Provide data access to business analysts & IT professionals Data mining
- 9. 5. Clustering WHAT IS A CLUSTER….? A cluster is collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. WHAT IS CLUSTERING….? The process of organizing objects into groups whose members are similar in some way. Distance-based clustering & Conceptual clustering are some of the types of clustering…
- 10. Possible applications of Clustering Marketing Biology Libraries WorldWideWeb
- 11. Problems of clustering Problems Cant address all requirements adequately Large data items can cause time complexity The result can be interpreted in different ways If obvious distance measure does not exist defining it is not easy
- 12. Clustering algorithms Exclusive Overlapping Hierarchical Probabilistic Classification of Clustering Algorithms
- 13. K-means Clustering Original data K-means clustering Clustering on “mouse” data set K-means is as iterative clustering algorithm in which items are moved among sets of clusters until the desired set is reached. This definition assumes that each ‘tuple’ has only one numeric value as apposed to a ‘tuple’ with many attribute values.
- 14. K-means algorithm Input: • D = {t1,t2,……..tn} //set of elements • k //Number of desired clusters Output: • K //Set of clusters Assign initial values for means m1,m2………..mk; Repeat Assign each item ti to the cluster which has the closest mean; Calculate the new mean for each cluster; Until
- 15. ---Example--- k = 2 {2,4,10,12,3,20, 30,11,25} I N P U T Output m1 m2 K1 K2 2 4 {2,3} {4,10,12,20,30 ,11,25} 2.5 16 {2,3,4} {10,12,20,30,1 1,25} 3 18 {2,3,4,10} {12,20,30,11,2 5} 4.75 19.6 {2,3,4,10,11,12} {20,30,25} 7 25 {2,3,4,10,11,12} {20,30,25}
- 17. So we conclude with...
- 19. ThankYou