business intelligence‚ data warehouse‚ data mining‚ text and web mining‚ and knowledge management. Justify and synthesis your answers/viewpoints with examples (e.g. eBay case) and findings from literature/articles. To understand the relationships between these terms‚ definition of each term should be illustrated. Firstly‚ business intelligence (BI) in most resource has been defined as a broad term that combines many tools and technologies‚ used to extract useful meaning of enterprise data in order to help
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Introduction to Data Mining Assignment 1 Ex1.1 what is data mining? (a) Is it another hype? Data mining is Knowledge extraction from data this need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. So‚ data mining definitely is not another hype it can be viewed as the result of the natural evolution of information technology. (b) Is it a simple transformation of technology developed
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credit customers is selected with data collected on the following five variables: 1. LOCATION (Rural‚ Urban‚ Suburban) 2. INCOME (in $1‚000 ’s – be careful with this) 3. SIZE (Household Size‚ meaning number of people living in the household) 4. YEARS (the number of years that the customer has lived in the current location) 5. CREDIT BALANCE (the customers current credit card balance on the store ’s credit card‚ in $). |PROJECT PART A: Exploratory Data Analysis
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Data Warehouse Concepts and Design Contents Data Warehouse Concepts and Design 1 Abstract 2 Abbreviations 2 Keywords 3 Introduction 3 Jarir Bookstore – Applying the Kimball Method 3 Summary from the available literature and Follow a Proven Methodology: Lifecycle Steps and Tracks 4 Issues and Process involved in Implementation of DW/BI system 5 Data Model Design 6 Star Schema Model 7 Fact Table 10 Dimension Table: 11 Design Feature: 12 Identifying the fields from facts/dimensions: MS: 12 Advanced
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Simply use statistics as a tool. You will be given a data. (Next year you will not be given data‚ you will gather data yoruself). 1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not. 2. Some comments on correlation: volume vs. horse power etc. 3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph
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and to make it as relatable and as descriptive as possible. One of the tools used was a meter In poetry‚ meter is the basic rhythmic structure of a verse or lines in verse. Many traditional verse forms prescribe a specific verse metre‚ or a certain set of metres alternating in a particular order. They also used Repetition of a sound‚ syllable‚ word‚ phrase‚ line‚ stanza‚ or metrical pattern which is a basic unifying device in all poetry the writer is usually trying to express an emotion or a phrase
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a set is a collection of distinct objects‚ considered as an object in its own right. For example‚ the numbers 2‚ 4‚ and 6 are distinct objects when considered separately‚ but when they are considered collectively they form a single set of size three‚ written {2‚4‚6}. an element‚ or member‚ of a set is any one of the distinct objects that make up that set A number‚ letter‚ point‚ line‚ or any other object contained in a set. There are two ways of describing‚ or specifying the members
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| 70 | 29 | E | 22 | 6 | F | 27 | 15 | G | 28 | 17 | H | 47 | 20 | I | 14 | 12 | J | 68 | 29 | | | | | | | a) draw a scatter diagram of number of sales calls and number of units sold b) Estimate a simple linear regression model to explain the relationship between number of sales calls and number of units sold y=2.139x-1.760 Number of units sold=2.139Number of units sold-1.760 c) Calculate and interpret the coefficient of correlation r=0.853=0.9236 (There
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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Censored data & Truncated data Censoring occurs when an observation or a measurement is outside the range and people don’ t know the certain value. The value is always above or below the range that people set. However‚ truncated data means that because of the limits‚ such as time‚ or space‚ people lose some data. Truncation is to cut off the data. In other words‚ we have collected and use the data‚ but the data is not in the range we have. It is called censored data. We don’t use the data because
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