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    Data mining

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    University CS 450 Data Mining‚ Fall 2014 Take-Home Test N#1 Date: September 22nd‚ 2014 Final deadline for submission September 29th‚ 2014 Weighting: 5% Total number of points: 100 Instructions: 1. Attempt all questions. 2. This is an individual test. No collaboration is permitted for assessment items. All submitted materials must be a result of your own work. Part I Question 1 [20 points] Discuss whether or not each of the following activities is a data mining task.

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    Data Mining

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    Nagham Hamid‚ Abid Yahya‚ R. Badlishah Ahmad & Osamah M. Al-Qershi Image Steganography Techniques: An Overview Nagham Hamid University Malaysia Perils (UniMAP) School of Communication and Computer Engineering Penang‚ Malaysia nagham_fawa@yahoo.com Abid Yahya University Malaysia Perlis (UniMAP) School of Communication and Computer Engineering Perlis‚ Malaysia R. Badlishah Ahmad University Malaysia Perlis (UniMAP) School of Communication and Computer Engineering Perlis‚ Malaysia

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    Data Mining

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    Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business ’s product or also in winning additional customers that may be purchasing from the competitor. Generally‚ data are any “facts‚ numbers‚ or text that can be processed by a computer.” Today

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    data communications

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    CHAPTER 12 ROUTING IN SWITCHED NETWORKS A NSWERS TO Q UESTIONS 12.1 The average load expected over the course of the busiest hour of use during the course of a day. 12.2 The tradeoff is between efficiency and resilience. 12.3 A static routing strategy does not adapt to changing conditions on the network but uses a fixed strategy developed ahead of time. With alternate routing‚ there are a number of alternate routes between source and destination and a dynamic choice of routes is

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    The Fukushima Nuclear Power Plant accident caused ecological problems. Abstract The Fukushima nuclear accident triggered a large-scale nuclear leak‚ which has had an immeasurable impact on the global ecological environment. This article addresses‚ in depth‚ features of the Fukushima Nuclear Power Plant accident: during the incident‚ the impact of the incident‚ and aspects of the aftermath. Through an analysis of the Fukushima Nuclear Power Plant accident‚ certain referential and significant

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    Customer Data

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    Enhancing Customer Data Enhanced Customer Data Repository is a secure and fully supported data repository with problem determination tools and functions. It updates problem management records (PMR) and maintains full data life cycle management. · combination of all the internal structured business data (CRM‚ ERP‚ POS and all the internal system data) and external unstructured data ( Social media data‚ feedback surveys‚ Audios‚ Videos‚ streaming data‚ Call center data‚ images) · unmanageable volumes

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    TYPES OF DATA AND COMPONENTS OF DATA STRUCTURES Data types 1. Primitive: is a data type provided by a programming language as a basic building block 2. Composite: is any data type which can be constructed in a program using its programming language’s primitive data types and other composite types 3. Abstract: is a mathematical model for a certain class of data structures that have similar behavior; or for certain data types of one or more programming languages that have similar semantics

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    Data Gathering

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    Data Gathering ➢ used to discover business information details to define the information structure ➢ helps to establish the priorities of the information needs ➢ further leads to opportunities to highlight key issues which may cross functional boundaries or may touch on policies or the organization itself ➢ highlighting systems or enhancements that can quickly satisfy cross-functional information needs ➢ a complicated task especially in a large and complex system ➢ must

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    Big Data

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    Problems and Opportunities created by having too much data‚ and what to do about them There is rarely an instance of business that you can encounter that does not involve the processing of data on information systems these days. Businesses and organizations use information systems in a majority of their functions‚ and as a result‚ are creating mass amounts of data. Because data is so crucial to business operations‚ it is being gathered‚ stored‚ and utilized in exponential amounts compared to

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    DATA CLUSTERING

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    WORLD DATA CLUSTERING ADEWALE .O . MAKO DATA MINING INTRODUCTION: Data mining is the analysis step of knowledge discovery in databases or a field at the intersection of computer science and statistics. It is also the analysis of large observational datasets to find unsuspected relationships. This definition refers to observational data as opposed to experimental data. Data mining typically deals with data that has already been collected for some purpose or the other than the data mining

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