1. How is a data mining different from a database2. Present

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1. How is a data mining different from a database? 2. Present an example where data mining is crucial to the success of a business. 3. Explain the difference and similarity between discrimination and classification, between characterization and clustering, and between classification and regression 4. Describe three challenges to data mining a regarding data mining methodology and user interaction issue. Outline the major research challenge of data mining in one specific application domain, such as stream/sensor data analysis, spatiotemporal data analysis, or bioinformatics 6. Briefly outline how to compute the dissimilarity between objects described by the following: (a) Nominal attributes (b) Binary attributes (c) Numeric attributes 7. Briefly outline how to compute the visualization techniques described by the following: (a) Pixel-oriented (b) Geometric-based (c) Parallel coordinate ...

Education

This document outlines an introductory assignment on data mining concepts. It asks the student to define key terms related to data mining like characterization, discrimination, association, classification, regression, clustering, and outlier analysis. It also asks the student to explain the differences and similarities between related terms, provide examples of how data mining is used in business, and outline challenges in data mining methodology, interacting with large datasets, and specific application domains. The assignment aims to give students foundational knowledge of data mining concepts and processes.

What is data In your address the.docx

write31

Data mining is the result of the evolution of database technology, machine learning research, statistics, and pattern recognition. It involves a multi-step process of knowledge discovery from large datasets. A data warehouse stores aggregated historical data for analysis, while a database stores transactional data and is optimized for rapid insertion, retrieval, and deletion. The major challenges of mining huge datasets compared to small datasets are the increased computational requirements and issues around scalability. Outliers, often considered noise, can provide valuable insights when detected and analyzed properly in certain domains like fraud detection.

KM.doc

butest

Knowledge mining is a process of extracting patterns and relationships from databases to improve decision-making. It has various applications including business intelligence, product design, manufacturing, and research profiling. The document then provides three examples: 1) A financial company used knowledge mining to analyze customer data and target marketing promotions. 2) Engineers have used it to help design products by matching requirements to existing part designs. 3) Researchers have used text mining to map relationships between topics in academic literature and identify active individuals and organizations.

D 17

nishantgaurav23

This document outlines exam questions for an Advanced Database Management Systems exam. It covers topics such as data mining vs knowledge discovery in databases, index implementations in PostgreSQL, database normalization, granularity and security/performance, semantic databases, shadow paging, clustering vs classification, database queries, views, XML documents, data dictionaries, multi-version schemes, and the Apriori algorithm for association rule mining. The exam questions require students to define concepts, provide examples, and demonstrate techniques like database normalization and joining normalized relations.

Data Mining Presentation.pptx

ChingChingErm

The document provides an introduction to data mining. It defines data mining as analyzing data to understand the past and predict the future. It discusses how data mining combines statistics, machine learning, artificial intelligence, and databases. It also provides brief histories of data mining and database technology. Finally, it describes common data mining tasks like classification, prediction, regression, clustering, time series analysis, and summarization.

Humanmetrics Jung Typology Test™You haven’t answered 1 que

NarcisaBrandenburg70

Humanmetrics Jung Typology Test™ You haven’t answered 1 question. The reliability of the results has decreased. You may click your browser’s "Back" button to answer the remaining questions. Your Type ISTJ Introvert(31%) Sensing(7%) Thinking(6%) Judging(16%) You have moderate preference of Introversion over Extraversion (31%) You have slight preference of Sensing over Intuition (7%) You have slight preference of Thinking over Feeling (6%) You have slight preference of Judging over Perceiving (16%) How Do You Want to Leverage The Type? Self-development ISTJ Type Description ISTJs are often called "inspectors". They have a keen sense of right and wrong, especially in their area of interest... Read full description » ISTJ Careers Career choices for your type Communication skills Learning style Famous ISTJs Click to view » Business use Staff Development & Teamwork Use advanced Jungian typology to improve collaboration, become better leader, and manage conflicts. Learn how » http://www.humanmetrics.com/personality/istj http://www.humanmetrics.com/personality/istj http://www.humanmetrics.com/personality/istj http://www.humanmetrics.com/personality/istj http://www.humanmetrics.com/personality/istj-careers?EI=-31&SN=7&TF=6&JP=16 http://www.humanmetrics.com/personality/istj-careers?EI=-31&SN=7&TF=6&JP=16 http://www.humanmetrics.com/personality/istj-careers?EI=-31&SN=7&TF=6&JP=16 http://www.humanmetrics.com/personality/istj-communication-style http://www.humanmetrics.com/personality/istj-learning-style http://www.humanmetrics.com/personality/famous-istjs http://www.humanmetrics.com/personality/istj-careers?EI=-31&SN=7&TF=6&JP=16 http://www.humanmetrics.com/personality/staff-development http://www.humanmetrics.com/personality/staff-development http://www.humanmetrics.com/personality/staff-development http://www.humanmetrics.com/personality/staff-development Pre-employment Testing Identify candidate's strengths, conduct effective interviews and assess compatibility. Learn how » Those who took this test also took these tests: Jung Marriage Test™ Write down your and your partner's personality type letters and percentages shown above and check your compatibility . Use this result as input to the Marriage Test now: Use as Partner A » Use as Partner B » Risk Attitudes Profiler™ Why troubles stick to you? Can you take control of your fortune? Leverage your risk type for success. Entrepreneur Quiz™ Find the most favorable for you businesses and franchises with Entrepreneur Quiz™. Role Model Quiz™ Intentionally or unconsciously, we try to follow our role model's behavior, although often our behavioral traits do not match very well those of our role model. Find which celebrities have personalities that are similar to yours. Get access to all of these tests at a discounted price - Click Here http://www.humanmetrics.com/personality/pre-employment-testing http://www.humanmetrics.com/personality/pre-employment-testing http://www.huma ...

A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...

Editor IJCATR

In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.

Data mining

Daminda Herath

This document provides an overview of data mining. It defines data mining as a process that takes data as input and outputs knowledge. The data mining process involves preparing data, applying data mining algorithms to identify patterns, and evaluating the results. The document discusses the motivation for data mining, including the growth of data and need to analyze unstructured data. It outlines common data mining tasks like classification, regression, association rule mining, clustering, and text and link analysis. The tasks of classification and regression are described in more detail.

This document provides an overview of data mining, including definitions, processes, tasks, and algorithms. It defines data mining as a process that takes data as input and outputs knowledge. The main steps in the data mining process are data preparation, data mining (applying algorithms to identify patterns), and evaluation/interpretation. Common data mining tasks are classification, regression, association rule mining, clustering, and text/link mining. Popular algorithms described are decision trees, rule-based classifiers, artificial neural networks, and nearest neighbor methods. Each have advantages and disadvantages related to predictive power, speed, and interpretability.

NPA Data Science: Outcomes and PCs

Kate Farrell

How Partitioning Clustering Technique For Implementing...

Nicolle Dammann

This document discusses and compares different clustering and conjoint analysis techniques. It provides definitions and examples of cluster analysis and conjoint analysis, highlighting their advantages and limitations. Cluster analysis is used to group similar customers and products for market segmentation. Conjoint analysis determines how people value different attributes of a product or service. The document also compares these two techniques and discusses their various applications in business and marketing research.

03Preprocessing01.pdf

Alireza418370

This document provides an overview of data preprocessing techniques. It discusses data quality issues like missing values, noise, and inconsistencies that require cleaning. Major tasks in preprocessing include data cleaning, integration, reduction, and transformation. Data cleaning techniques are described for handling incomplete, noisy, and inconsistent data. Methods for data integration, reduction through dimensionality reduction and sampling, and transformation through normalization and discretization are also summarized.

IRJET- Retrieval of Images & Text using Data Mining Techniques

IRJET Journal

This document discusses using data mining techniques like clustering and association rule mining for image retrieval. It proposes a system that extracts both visual features (e.g. color, texture) and textual features from images. The features are clustered separately, then association rules are mined by fusing the clusters. Strong association rules are selected as training data. A query image's features are mined to find matching rules to retrieve semantically related images from the database. This combines content-based and text-based retrieval to address limitations of each approach individually.

Challenges Faced by Novices While Developing and Designing the Visualization ...

IRJET Journal

This document discusses challenges faced by novices in developing visualizations using business intelligence (BI) tools. It finds that novices struggle with three main activities: selecting appropriate data attributes based on the question, choosing a suitable visual template, and interpreting complex visualizations. Common problems include high visual complexity, unfamiliar visualization types, inappropriate scaling, and difficulties understanding measurement semantics. Novices also tend to incompletely or improperly specify visualization details. They prefer simple charts like bar, line and pie charts that they are familiar with. The development process faces barriers at each stage of selecting data, creating visual mappings, and interpretation.

Cs 1004 -_data_warehousing_and_data_mining

hari91

This document contains 41 questions and answers related to data warehousing and data mining. Some key topics covered include: the uses of statistics in data mining, factors to consider when selecting a sample in statistics, types of databases like relational and transactional databases, the steps in the data mining process, definitions of data cleaning and data mining, descriptive versus predictive data mining, and an overview of statistical analysis assumptions and probabilistic graphical models.

AS ICT (OCR) G061 3.1.1 Data, Information, Knowledge & Processing booklet

Christos Demetriou

This document contains questions and activities from a lesson plan on data, information, and knowledge processing. It covers topics like data types, sources, quality, encoding, validation, verification, backup, archiving, and costs. Some key points include: - Discussing different data types like boolean, integer, string and their appropriate uses. - Identifying direct and indirect sources of data and how they differ. - Factors that affect data quality like accuracy, relevance, completeness. - Methods of encoding, validating and verifying data to ensure quality. - The importance of backing up and archiving data for protection and long-term storage. - Various costs involved in producing information like hardware, software

Large Graph Mining

Sabri Skhiri

With the recent growth of the graph-based data, the large graph processing becomes more and more important. In order to explore and to extract knowledge from such data, graph mining methods, like community detection, is a necessity. The legacy graph processing tools mainly rely on single machine computational capacity, which cannot process large graphs with billions of nodes. Therefore, the main challenge of new tools and frameworks lies on the development of new paradigms that are scalable, efficient and flexible. In this paper, we review the new paradigms of large graph processing and their applications to graph mining domains using the distributed and shared nothing approach used for large data by Internet players.

Machine learning topics machine learning algorithm into three main parts.

DurgaDeviP2

Machine learning topics machine learning algorithm into three main parts. A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data. An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model. A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this iterative “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.

International Refereed Journal of Engineering and Science (IRJES)

irjes

11.challenging issues of spatio temporal data mining

Alexander Decker

This document discusses the challenging issues of spatio-temporal data mining. It begins with an introduction to spatio-temporal databases and how they differ from traditional databases by managing moving objects and their locations over time. It then provides an overview of spatial data mining and temporal data mining before focusing on spatio-temporal data mining, which aims to analyze large databases containing both spatial and temporal information. The document outlines some of the key challenges in applying traditional data mining techniques to spatio-temporal data due to its continuous and correlated nature.

Data mining and warehousing qb

aniprahal

This document contains a question bank for a data warehousing and mining course. It includes questions ranging from 2 to 16 marks covering topics such as data warehousing concepts, OLAP operations, data pre-processing, association rule mining, classification, clustering, and applications of data mining. The questions cover key concepts, algorithms, and processes within each topic area. Examples are provided for applying algorithms such as Apriori, FP-Growth, ID3, k-means and k-medoids to sample datasets.

A SURVEY ON DATA MINING IN STEEL INDUSTRIES

IJCSES 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.

sad.pdf

Simrankaur3503

This document contains information about a systems analysis and design exam, including: - The exam will be 3 hours long and worth a total of 100 marks. - Students must answer 5 questions, selecting 2 from Group A, 2 from Group B, and all questions from Group C. - Answers should be brief, include diagrams where relevant, and avoid unnecessary detail. - The document provides sample exam questions covering topics like system modeling, feasibility analysis, database design, and testing strategies.

Running Head PROJECT DELIVERABLE 31PROJECT DELIVERABLE 310.docx

todd581

Running Head: PROJECT DELIVERABLE 31 PROJECT DELIVERABLE 310 Project Deliverable 3: Database and Programming Design Leo Austin Professor Joe Scott CIS498 – Information Technology Capstone 08/22/2018 Introduction Bicycle Trader being a constantly growing internet-based company requires the collection of an abundance of data to analyze for continued operations. Whether customers signup for services or browse through the website, data is gathered to allow the website to adapt to demands and cater to the customers’ needs and determine what will make using the site more user-friendly. Most importantly is the need to gather data in order to facilitate the entry and archiving of customer input data and use by other entities or departments within the business. Various database models can be taken into consideration for the needs of this business, and the relational database model is the most applicable due to the data sorting requirements for the website. Not only is the rational database model the ideal database solution, but because they primarily consist of tables used to manage and store data, they are relatively easy to create and maintain. Many organizations choose this approach as it facilitates access to understandable data assets. Separating data by implementing tables also allows for the ability to adequately secure data by distinguishing each with their own classifications. Sorting data into tables also means that data can be added or withdrawn without having to overhaul the entire database. Implementing data warehousing alongside relational databases provides further practicality and presents many advantages. By doing so, we can take advantage of its ability to “store large quantities of historical data and enable fast, complex queries across all the data, typically using Online Analytical Processing (OLAP)” (Panoply, n.d.). Data warehouses are essentially a collection of data from various sources that can be used by organizations for reporting and analysis. Because of the nature of Bicycle Trader and the abundance of like items that will be sold be by users on the website, a data warehouse will be the most practical solution for archiving data, because unlike most databases which normalize data in order to eliminate redundant data, a data warehouse uses a denormalized data structure. This means that fewer data tables with more grouping are used and redundancies aren’t excluded. This combination of relational data systems, the data warehouse and relational database, can be hosted internally by the organization on its’ mainframe, and stored in their cloud. Using a cloud yields more advantages as it is the easiest and most cost-effective approach. By using this method, data can easily be accessed from several locations. Additionally, this allows for fewer physical resources as it eliminates some of the costs associated with expensive systems and equipment, expert staff, and energy consumption by alternatively utilizing the .

Data Mining: Concepts and techniques: Chapter 13 trend

Salah Amean

Data Analytics Course Curriculum_ What to Expect and How to Prepare in 2023.pdf

Neha Singh

New approaches of Data Mining for the Internet of things with systems: Litera...

IRJET Journal

This document provides a literature review of new approaches to data mining for the Internet of Things (IoT). It discusses data mining from three perspectives: data view, method view, and application view. In the data view, it examines data mining functions like classification, clustering, association analysis, time series analysis, and anomaly detection. In the method view, it reviews machine learning, statistical, and deep learning techniques used for data mining. In the application view, it discusses data mining applications in industries like business, healthcare, and government. It also discusses challenges of big data mining for IoT and proposes a framework for large-scale IoT data mining using open source tools.

Service Level Comparison for Online Shopping using Data Mining

IIRindia

The term knowledge discovery in databases (KDD) is the analysis step of data mining. The data mining goal is to extract the knowledge and patterns from large data sets, not the data extraction itself. Big-Data Computing is a critical challenge for the ICT industry. Engineers and researchers are dealing with the cloud computing paradigm of petabyte data sets. Thus the demand for building a service stack to distribute, manage and process massive data sets has risen drastically. We investigate the problem for a single source node to broadcast the big chunk of data sets to a set of nodes to minimize the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed data centers. The Big-data broadcasting problem is modeled into a LockStep Broadcast Tree (LSBT) problem. And the main idea of the LSBT is defining a basic unit of upload bandwidth, r, a node with capacity c broadcasts data to a set of [c=r] children at the rate r. Note that r is a parameter to be optimized as part of the LSBT problem. The broadcast data are further divided into m chunks. In a pipeline manner, these m chunks can then be broadcast down the LSBT. In a homogeneous network environment in which each node has the same upload capacity c, the optimal uplink rate r, of LSBT is either c=2 or 3, whichever gives the smaller maximum completion time. For heterogeneous environments, an O(nlog2n) algorithm is presented to select an optimal uplink rate r, and to construct an optimal LSBT. With lower computational complexity and low maximum completion time, the numerical results shows better performance.The methodology includes Various Web applications Building and Broadcasting followed by the Gateway Application and Batch Processing over the TSV Data after which the Web Crawling for Resources and MapReduce process takes place and finally Picking Products from Recommendations and Purchasing it.

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1. How is a data mining different from a database2. Present

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