the stock prices by using trends‚ patterns‚ moving averages observed from historical data. However‚ there have been a certain number of people criticizing the use of past data. Among these people‚ a French mathematician‚ Louis Bachelier raised a theory called Efficient Market Hypothesis more than a century ago. The theory states that stock prices follow a random walk‚ which discouraged the study of historical data. This is very controversial and has led to an ever lasting dispute about the reliability
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ASKARI DANIYAL ARSHAD 2 OUTLINE DBMS DATA MINING APPLICATIONS RELATIONSHIP 3 DATA BASE MANAGEMENT SYSTEM A complete system used for managing digital databases that allow storage of data‚ maintenance of data and searching data. 4 DATA MINING Also known as Knowledge discovery in databases (KDD). Data mining consists of techniques to find out hidden pattern or unknown information within a large amount of raw data. 5 EXAMPLE An example to make it more
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DATA MINING REPORT A Comparison of K-means and DBSCAN Algorithm Data Mining with Iris Data Set Using K-Means Cluster method within Weak Data Mining Toolkit. Team Task ......................................................................................................................................... 3 1.0 Introduction ................................................................................................................................. 3 2.0 Related Works ................
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Overview: Chapter 2 Data Mining for Business Intelligence Shmueli‚ Patel & Bruce Core Ideas in Data Mining Classification Prediction Association Rules Data Reduction Data Visualization and exploration Two types of methods: Supervised and Unsupervised learning Supervised Learning Goal: Predict a single “target” or “outcome” variable Training data from which the algorithm “learns” – value of the outcome of interest is known Apply to test data where value is not known and will be predicted
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Best Ways to Analyze Data in Order to Improve Decision-Making Descriptive Analysis: Defined as quantitatively describing the main features of a collection of information. Descriptive analysis are distinguished from inferential analysis (or inductive analysis)‚ in that descriptive analysis aim to summarize a sample‚ rather than use the data to learn about the population that the sample of data is thought to represent. Two types of descriptive measures are: 1. Measures of central tendency: used
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Running head: BUSINESS STRATEGY Business Strategy and the Importance of Data-Driven Decision Making Business Strategy and the Importance of Data-Driven Decision Making Good decision making is arguably the most important skill a successful manager can possess‚ but the ability to make intelligent decisions on an on-going basis requires not only intuition and experience‚ but also the right data. In fact‚ Garrison‚ Noreen‚ and Brewer (2012) identify intelligent
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watching the Data-Based Decision Making webinar presented by Amy Elledge‚ I am more aware of the importance of collecting data and how to use it to properly address students’ needs. Data-Based Decision Making is one of the most important factors/roles within Response to Intervention/Instruction (RTI). It is an essential component of RTI. If not utilized using logic and with a purpose/goal in mind‚ the data that is being/has been collected will be no good. Data-Based Decision Making can and will
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Chapter 3 – Data Visualization Chapter 4 – Summary Statistics Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Data Visualization • “A picture is worth a thousand words” • Data visualization and summary statistics help condense data • Effective presentation • Supports data cleaning (identify missing values‚ outliers‚ incorrect values‚ duplicates) and exploring (combine some groups) • Helps identify suitable variables • Mandatory initial step for
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measure of the cube is count. 1‚ How many nonempty cuboids will a full data cube contain? Answer: 210 = 1024 2‚ How many nonempty aggregate (i.e.‚ non-base) cells will a full cube contain? Answer: There will be 3 ∗ 210 − 6 ∗ 27 − 3 = 2301 nonempty aggregate cells in the full cube. The number of cells overlapping twice is 27 while the number of cells overlapping once is 4 ∗ 27 . So the final calculation is 3 ∗ 210 − 2 ∗ 27 − 1 ∗ 4 ∗ 27 − 3‚ which yields the result. 3‚ How many nonempty aggregate cells
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Company Information Applebee’s: Applebee’s International‚ Inc.‚ is an American company which develops‚ franchises‚ and operates the Applebee’s Neighborhood Grill and Bar restaurant chain. As of September 2011‚ there were 2‚010 restaurants operating system-wide in the United States‚ one U.S. territory and 14 other countries. The company is headquartered in Kansas City‚ Missouri after moving from Lenexa‚ Kansas in September 2011. The Applebee’s concept focuses on casual dining‚ with mainstream American
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