chapter 3 introduction to predictive modeling from correlation to supervised segmentation
Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining potx
... data to better understand its characteristics Key motivations of data exploration include – Helping to select the right tool for preprocessing or analysis – Making use of humans’ abilities to recognize ... patterns not captured by data analysis tools Related to the area of Exploratory Data Analysis (EDA) – Created by statistician John Tukey – Seminal book is Exploratory Data Analysis by Tukey – A nice ... Introduction to Data Mining Iris Sample Data Set Many of the exploratory data techniques are illustrated with the Iris Plant data set – Can be obtained from the UCI Machine Learning Repository http://www.ics.uci.edu/~mlearn/MLRepository.html...
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Real-Time Digital Signal Processing - Chapter 1: Introduction to Real-Time Digital Signal Processing
... breakpoint The command to enable a breakpoint can be given from the Toggle Breakpoint hot button on the project toolbar or by clicking the right mouse button and choosing toggle breakpoint The ... project-viewing window to open it from the source folder ± Adding and removing a breakpoint to a specific line is quite simple To add a breakpoint, move the cursor to the line where we want to set a breakpoint ... PC to DSP memory on the target, or write processed data samples to the host PC In the following experiment, we will learn how to set up a probe point to transfer data from the example program to...
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Real-Time Digital Signal Processing - Chapter 2: Introduction to TMS320C55x Digital Signal Processor
... try to access the accumulators in the DU via the single link, it creates a conflict mov AC0, AR2 j jcall AC3 To solve the problem, we can change the subroutine call from call by accumulator to ... exp1.cmd, from previous experiment, rename it to exp2.cmd and save it to A: \Experiment2 From the CCS Project-Options-Linker-Library tab, to include the run-time support library rst55.lib From CCS ... tells the assembler to begin assembling source code or data into that section It is often used to separate long programs into logical partitions It can separate the subroutines from the main program,...
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Tài liệu Module 3: Introduction to COM+ pdf
... how to add components to the application you have created ! Create a COM+ application Start the Component Services tool • On the Start menu, point to Programs, then point to Administrative Tools, ... the comment Find out the Total value of the order b Assign a value to curTotal by passing rsOrderDetails to the GetTotal function Use the private Authorize function to authorize the order and ... is to enable applications to be constructed from preassembled parts or components Such applications are easier to maintain and change than the so-called monolithic applications built prior to...
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Chapter 4 Introduction to Portfolio Theory
... investments in the two stocks The investor s problem is to decide how much wealth to put in asset A and how much to put in asset B Let xA denote the share of wealth invested in stock A and xB denote ... simple way to solve this problem is to substitute the restriction (7) into the function (6) and reduce the problem to a minimization over one variable To illustrate, use the restriction (7) to solve ... standard deviation (risk) from forming a diversi& portfolio The meaning to an investor of the reduction in standard deviation ed is not as clear as the meaning to an investor of the increase in...
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Chapter 15 introduction to the design of electric machinery
... similar to the stator backiron The inert region (radii from rrs to rri) mechanically transfers torque from the rotor backiron to the shaft It is often just a continuation of the rotor Stator Backiron ... diameter from 1.38 to 1.87 mm This lowers the machine resistance, but increases conductor mass from 0.914 to 1.64 kg and stator mass from 4.12 to 6.40 kg The increase in stator mass is in order to ... radius, rrb—the rotor backiron radius, rrg—the rotor air-gap radius, rst—the stator tooth inner radius, rsb—the stator backiron inner radius, and rss—the stator shell radius A stator shell, if present,...
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Module 3 Introduction to Information and Communication Technologies (ICTs) pdf
... The objective of Module • To improve the library staff to know about how to use ICts as computer including using internet … The level of student • Students who will come to study might have basic ... Module Introduction to Information and Communication Technologies (ICTs) - Lesson 1: Why librarians need to know about ICTs and acquire skill in their use? -...
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Chapter 1 Introduction to Routing and Packet ForwardingRouting Protocols and Concepts quangkien@gmail.com.Topicsl Inside the Router Ÿ Routers are computers Ÿ Router CPU and Memory Ÿ Internetwork Operating System Ÿ Router Bootup Process Ÿ Router Ports doc
... exactly what happens to data as it moves from source to destination Ÿ Review the packet and frame field specifications Ÿ Discuss in detail how the frame fields change from hop to hop, whereas the ... Dynamic Routing Protocols: RIP and OSPF l RIP uses hop count Ÿ R1 to R3 Ÿ Fewer links but much slower l OSPF uses bandwidth Ÿ R1 to R2 to R3 Ÿ More routers but much faster links 31 To reach the 192.168.1.0/24 ... the following steps to connect a terminal to the console port on the router: l Connect the terminal using the RJ-45 to RJ-45 rollover cable and an RJ-45 to DB-9 or RJ-45 to DB-25 adapter l Configure...
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Chapter 1 – Introduction to Computers and C++ Programming pot
... (stream extraction operator) • Used with std::cin • Waits for user to input value, then press Enter (Return) key • Stores value in variable to right of operator – Converts value to variable data type ... Operators in innermost pair first – Multiplication, division, modulus applied next • Operators applied from left to right – Addition, subtraction applied last Operator(s) Operation(s) Order to ... computer operations Clearer to humans Incomprehensible to computers – Translator programs (assemblers) • Convert to machine language Example: LOAD BASEPAY ADD OVERPAY STORE GROSSPAY © 2003 Prentice...
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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining ppt
... whether a customer is likely to be lost to a competitor – Approach: • Use detailed record of transactions with each of the past and present customers, to find attributes – How often the customer calls, ... © Tan,Steinbach, Kumar Total Articles 555 354 278 Introduction to Data Mining 19 Clustering of S&P 500 Stock Data Observe Stock Movements every day Clustering points: Stock-{UP/DOWN} Similarity ... {Bagels, … } > {Potato Chips} – Potato Chips as consequent => Can be used to determine what should be done to boost its sales – Bagels in the antecedent => Can be used to see which products...
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Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining potx
... Points Introduction to Data Mining 500 Points 32 Sample Size What sample size is necessary to get at least one object from each of 10 groups © Tan,Steinbach, Kumar Introduction to Data Mining 33 ... © Tan,Steinbach, Kumar Introduction to Data Mining 13 Document Data Each document becomes a `term' vector, – each term is a component (attribute) of the vector, – the value of each component is ... – duplicate data © Tan,Steinbach, Kumar Introduction to Data Mining 21 Noise Noise refers to modification of original values – Examples: distortion of a person’s voice when talking on a poor phone...
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
... Determine how to split the records • How to specify the attribute test condition? • How to determine the best split? – Determine when to stop splitting © Tan,Steinbach, Kumar Introduction to Data Mining ... Determine how to split the records • How to specify the attribute test condition? • How to determine the best split? – Determine when to stop splitting © Tan,Steinbach, Kumar Introduction to Data Mining ... condition? • How to determine the best split? – Determine when to stop splitting © Tan,Steinbach, Kumar Introduction to Data Mining 46 Stopping Criteria for Tree Induction Stop expanding a node...
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Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf
... of a person may vary from 1.5m to 1.8m • weight of a person may vary from 90lb to 300lb • income of a person may vary from $10K to $1M © Tan,Steinbach, Kumar Introduction to Data Mining 43 Nearest ... Introduction to Data Mining 41 Nearest Neighbor Classification… Choosing the value of k: – If k is too small, sensitive to noise points – If k is too large, neighborhood may include points from other ... nearest neighbors to retrieve To classify an unknown record: – Compute distance to other training records – Identify k nearest neighbors – Use class labels of nearest neighbors to determine the...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... Use efficient data structures to store the candidates or transactions – No need to match every candidate against every transaction © Tan,Steinbach, Kumar Introduction to Data Mining 11 Reducing ... Pointer to projected database of its ancestor node – Bitvector containing information about which transactions in the projected database contain the itemset © Tan,Steinbach, Kumar Introduction to ... Tan,Steinbach, Kumar Introduction to Data Mining 45 Rule Generation How to efficiently generate rules from frequent itemsets? – In general, confidence does not have an anti-monotone property c(ABC →D)...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
... Introduction to Data Mining 26 Examples of Sequence Data Sequence Database Sequence Element (Transaction) Event (Item) Customer Purchase history of a given customer A set of items bought by a customer ... Introduction to Data Mining 20 Multi-level Association Rules Food Electronics Bread Computers Milk Wheat Skim White Foremost © Tan,Steinbach, Kumar 2% Desktop Kemps Introduction to Data Mining Home Laptop ... consequent – Apply statistical test to determine interestingness of the rule © Tan,Steinbach, Kumar Introduction to Data Mining 13 Statistics-based Methods How to determine whether an association...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... Tan,Steinbach, Kumar Introduction to Data Mining What is not Cluster Analysis? Supervised classification – Have class label information Simple segmentation – Dividing students into different registration ... Original Points K-means Clusters One solution is to use many clusters Find parts of clusters, but need to put together © Tan,Steinbach, Kumar Introduction to Data Mining 43 Overcoming K-means Limitations ... belongs to every cluster with some weight between and – Weights must sum to – Probabilistic clustering has similar characteristics Partial versus complete – In some cases, we only want to cluster...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
... Results: CURE Picture from CURE, Guha, Rastogi, Shim © Tan,Steinbach, Kumar Introduction to Data Mining Experimental Results: CURE (centroid) (single link) Picture from CURE, Guha, Rastogi, Shim © Tan,Steinbach, ... (d) Introduction to Data Mining 13 Chameleon: Clustering Using Dynamic Modeling Adapt to the characteristics of the data set to find the natural clusters Use a dynamic model to measure the similarity ... keep the connections to the most similar (nearest) neighbors of a point while breaking the connections to less similar points The nearest neighbors of a point tend to belong to the same class as...
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