chapter 2 introduction to data structures and
Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining potx
... k =1 Introduction to Data Mining 49 Euclidean Distance point p1 p2 p3 p4 p1 p3 p4 p2 0 y 1 p1 p1 p2 p3 p4 x 2. 828 3.1 62 5.099 p2 2. 828 1.414 3.1 62 p3 3.1 62 1.414 p4 5.099 3.1 62 Distance Matrix ... Tan,Steinbach, Kumar Introduction to Data Mining 19 Ordered Data Spatio-Temporal Data Average Monthly Temperature of land and ocean © Tan,Steinbach, Kumar Introduction to Data Mining 20 Data Quality ... Kumar Introduction to Data Mining 27 Aggregation Variation of Precipitation in Australia Standard Deviation of Average Monthly Precipitation © Tan,Steinbach, Kumar Introduction to Data Mining Standard...
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
... | t ) ]2 j C1 C2 P(C1) = 0/6 = C1 C2 P(C1) = 1/6 C1 C2 P(C1) = 2/ 6 © Tan,Steinbach, Kumar P(C2) = 6/6 = Gini = – P(C1 )2 – P(C2 )2 = – – = P(C2) = 5/6 Gini = – (1/6 )2 – (5/6 )2 = 0 .27 8 P(C2) = 4/6 ... B? C1 Yes No C2 Gini = 0.500 Gini(N1) = – (5/6 )2 – (2/ 6 )2 = 0.194 Gini(N2) = – (1/6 )2 – (4/6 )2 = 0. 528 © Tan,Steinbach, Kumar Node N1 Node N2 C1 C2 N1 N2 Gini=0.333 Introduction to Data Mining ... (3/3 )2 – (0/3 )2 =0 Gini(N2) = – (4/7 )2 – (3/7 )2 = 0.489 © Tan,Steinbach, Kumar Node N2 C1 C2 N1 N2 Gini=0.361 C1 C2 Gini = 0. 42 Gini(Children) = 3/10 * + 7/10 * 0.489 = 0.3 42 Gini improves !! Introduction...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... Tan,Steinbach, Kumar Introduction to Data Mining 28 Maximal vs Closed Itemsets TID Items ABC ABCD BCE ACDE DE Transaction Ids null 124 123 A 12 124 AB 12 24 AC ABC B AE 24 ABD ABE 2 ACD BD 345 D BC ... Itemsets Minimum support = 124 123 A 12 124 AB 12 ABC 24 AC ABD ABE AE 345 D BC BD ACD 24 5 C 123 24 123 4 B AD Closed but not maximal null ACE BE ADE BCD E 24 CD BCE Closed and maximal 34 CE BDE 45 ... 357 689 Introduction to Data Mining 367 368 22 Subset Operation Using Hash Tree Hash Function transaction 1+ 23 56 2+ 356 12+ 356 1,4,7 3+ 56 3,6,9 2, 5,8 13+ 56 23 4 567 15+ 145 136 345 124 457...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
... Events 1 ,2, 4 2, 3 1 ,2 2,3,4 1, 2, 3,4 2, 4,5 3, 4, 1, 2, 4, Introduction to Data Mining Minsup = 50% Examples of Frequent Subsequences: < {1 ,2} > < {2, 3} > < {2, 4}> < {3} {5}> < {1} {2} > < {2} {2} > ... B B C Timestamp 10 20 23 11 17 21 28 14 © Tan,Steinbach, Kumar Events 2, 3, 6, 1 4, 5, 7, 8, 1, 1, 1, 8, Introduction to Data Mining 26 Examples of Sequence Data Sequence Database Sequence Element ... Tan,Steinbach, Kumar Introduction to Data Mining 15 Min-Apriori (Han et al) Document-term matrix: TID W1 W2 W3 W4 W5 D1 2 0 D2 0 2 D3 0 D4 0 1 D5 1 Example: W1 and W2 tends to appear together in the...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... Kumar Introduction to Data Mining 29 10 Clusters Example Iteration Iteration 4 2 y y 0 -2 -2 -4 -4 -6 -6 10 15 20 x 15 20 15 20 x Iteration Iteration 6 4 2 y y 10 0 -2 -2 -4 -4 -6 -6 10 15 20 x ... Kumar Introduction to Data Mining 31 10 Clusters Example Iteration Iteration 4 2 y y 0 -2 -2 -4 -4 -6 -6 10 15 20 x Iteration 15 20 15 20 6 4 2 y y 10 x Iteration 0 -2 -2 -4 -4 -6 -6 10 15 20 x ... -2 Introduction to Data Mining Sub-optimal Clustering 22 Importance of Choosing Initial Centroids Iteration 3 2. 5 y 1.5 0.5 -2 -1.5 -1 -0.5 0.5 1.5 x © Tan,Steinbach, Kumar Introduction to Data...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
... 19 82- 1994) 90 90 24 22 25 60 60 13 26 14 30 30 16 20 17 latitude latitude 21 15 18 0 19 -30 23 -30 -60 -60 11 -90 -180 12 -150 - 120 -90 -60 -30 30 60 90 -90 -180 -150 - 120 -90 10 120 150 180 -60 ... Introduction to Data Mining 22 Experimental Results: CURE (9 clusters) © Tan,Steinbach, Kumar Introduction to Data Mining 23 Experimental Results: CURE (15 clusters) © Tan,Steinbach, Kumar Introduction to ... Kumar Introduction to Data Mining 17 Experimental Results: CHAMELEON © Tan,Steinbach, Kumar Introduction to Data Mining 18 Experimental Results: CHAMELEON © Tan,Steinbach, Kumar Introduction to Data...
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Real-Time Digital Signal Processing - Chapter 2: Introduction to TMS320C55x Digital Signal Processor
... as data and communicate with other AU and PU registers The barrel shifter may be used to perform a data shift in the range of 2 32 (shift right 32- bit) to 23 1 (shift left 31-bit) 2. 2 .2 TMS 320 C55x ... exp2b and save it in A: \Experiment2 Use exp2.cmd, exp2b.c, exp2b_1.asm, exp2b _2. asm, exp2b_3 asm, and exp2b_4.asm to build the project Open the memory watch window to watch how the arrays Ai and ... generator to act as if the access is made to the main data page That is, XDP Example 2. 8: Instruction mov mmap(@AC0L), T0 AC0 T0 AC0 T0 00 12DF 020 2 0000 Before instruction 00 12DF 020 2 020 2 After...
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Tài liệu Module 1: Introduction to Data Warehousing and OLAP pptx
... relational data marts and OLAP cubes differ greatly in data storage, data content, data sources, data retrieval, and business analysis capabilities Data Storage Relational data marts and OLAP cubes ... Storage Data Storage Relational Relational Data Structure Data Structure N-dimensional N-dimensional Data structure Data structure Data Content Data Content Detailed and Detailed and Summarized Data ... in how they store data: ! Relational data marts store data in structures supported by relational database technologies ! OLAP cubes store data in multidimensional structures These structures can...
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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining ppt
... 341 26 0 National 27 3 36 Metro 943 746 Sports 738 573 Entertainment © Tan,Steinbach, Kumar Total Articles 555 354 27 8 Introduction to Data Mining 19 Clustering of S&P 500 Stock Data Observe Stock ... day © Tan,Steinbach, Kumar Introduction to Data Mining 28 Challenges of Data Mining Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy ... analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns © Tan,Steinbach, Kumar Introduction to Data Mining What is (not) Data Mining?...
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Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining potx
... provided on thetonext slide © Tan,Steinbach, Kumar Introduction Data Mining 21 Contour Plot Example: SST Dec, 1998 Celsius © Tan,Steinbach, Kumar Introduction to Data Mining 22 Visualization ... slides © Tan,Steinbach, Kumar Introduction to Data Mining 23 Visualization of the Iris Data Matrix standard deviation © Tan,Steinbach, Kumar Introduction to Data Mining 24 Visualization of the Iris ... Tan,Steinbach, Kumar 28 – Each object Introduction to Data Mining separate face becomes a Star Plots for Iris Data Setosa Versicolour Virginica © Tan,Steinbach, Kumar Introduction to Data Mining 29 Chernoff...
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Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf
... Repeat Step (2) and (3) until stopping criterion is met © Tan,Steinbach, Kumar Introduction to Data Mining 14 Example of Sequential Covering (ii) Step © Tan,Steinbach, Kumar Introduction to Data Mining ... Single 70K No Yes Married 120 K No No Divorced 95K Yes = | 2/ 4 – 1 /2 | + | 2/ 4 – 1 /2 | = No Married No d(Married,Divorced) Yes Divorced 22 0K No = | 0/4 – 1 /2 | + | 4/4 – 1 /2 | = No Single 85K Yes ... according to distance • weight factor, w = 1/d2 © Tan,Steinbach, Kumar Introduction to Data Mining 41 Nearest Neighbor Classification… Choosing the value of k: – If k is too small, sensitive to noise...
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Introduction to data structures
... program Data Structures in Alice Alice has two built-in data structures that can be used to organize data, or to create other data structures: • • Lists Arrays Lists A list is an ordered set of data ... Choosing Data Structures A binary tree is a good data structure to use for searching sorted data The middle item from the list is stored in the root node, with lesser items to the left and greater ... the structure of the data affects what can be done efficiently with the data Choosing Data Structures A queue is a good data structure to use for storing things that need to be kept in order,...
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INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING - CHAPTER 2 ppt
... training data are saved for each feature If standarderror normalizations are used, the means and standard errors for each feature are saved for application to new data 2. 2 .2 Data Smoothing Data smoothing ... faced by most data mining methods in searching for good solutions 2. 2 Data Transformations A central objective of data preparation for data mining is to transform the raw data into a standard spreadsheet ... error and significance sig is typically set to 2, A and B are the same feature measured for class and class 2, respectively, and nl and n2 are the corresponding numbers of cases If Equation (2. 4)...
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A Practical Introduction to Structure, Mechanism, and Data Analysis - Part 2 pptx
... [S] [S] e\IR R (2. 18) (2. 19) (2. 20) [P] : [S] (1 e\IR) (2. 21) R Hence, from Equations 2. 17 and 2. 21 we expect the concentrations of S and P to respectively decrease and increase exponentially, ... carbon bound to the oxygen atoms uses sp hybridization: it forms a bond to the other carbon, a bond to each oxygen atom, and one bond to one of the oxygen atoms Thus, one oxygen atom would have ... Enzymes: A Practical Introduction to Structure, Mechanism, and Data Analysis Robert A Copeland Copyright 20 00 by Wiley-VCH, Inc ISBNs: 0-471-35 929 -7 (Hardback); 0-471 -22 063-9 (Electronic) STRUCTURAL...
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INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING - CHAPTER 1 pdf
... KDD and Related Fields Data Mining Methods Why is KDD Necessary? KDD Applications Challenges for KDD Chapter Preprocessing Data 2. 1 2. 2 2. 3 2. 4 Data Quality Data Transformations Missing Data Data ... and papers used to design this course are followings: Chapter is with material from [7] and [5], Chapter is with [6], [8] and [14], Chapter is with [11] and [ 12] , Chapters and are with [4], Chapter ... [3], and Chapter is with [13] Knowledge Discovery and Data Mining Chapter Overview of knowledge discovery and data mining 1.1 What is Knowledge Discovery and Data Mining? Just as electrons and...
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INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING - CHAPTER 3 pot
... its parent and stop If all examples in T are negative, create a ‘N’ node with T as its parent and stop Select an attribute X with values v1, v2, …, vN and partition T into subsets T1, T2, …, TN ... hierarchical structures are still difficult to navigate and view even with tightly-coupled and fish-eye views To address the problem, we have been developing a special technique called T2.5D (Tree 2. 5 ... support and visualized structures are difficult to navigate, while 2D browsers have limitation in display many nodes in one view The T2.5D technique combines the advantages of both 2D and 3D drawing...
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INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING - CHAPTER 4 ppsx
... and detergent, OJ and soda, OJ and cleaner Milk and detergent, milk and soda, milk and cleaner Detergent and soda, detergent and cleaner Soda and cleaner This is a total of 10 counts The third ... applied, to analyze data and to get a start Most data mining techniques are not primarily used for undirected data mining Association rule analysis, on the other hand, is used in this case and provides ... People who buy 2- by-4s also purchase nails; customers who purchase paint buy paint brushes; oil and oil filters are purchased together as are hamburgers and hamburger buns, and charcoal and lighter...
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INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING - CHAPTER 5 docx
... than another The valedictorian has better grades than the salutatorian, but we don’t 65 Knowledge Discovery and Data Mining know by how much If X, Y, and Z are ranked 1, 2, and 3, we know that X ... measures, automatic clustering can be a plied to almost any kind of data It is as easy to find clusters in collections of new stories or insurance claims as in astronomical or financial data Automatic ... values of the four fields The vectors have the form (X1, X2, X3, X4) The value of X1 for the new centroid is the mean of all 20 0 Xls and similarly for X2, X3 and X4 Figure 5.3: Calculating the...
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INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING - CHAPTER 6 docx
... trying to optimize its performance on the testing and validation data Most commercial neural network tools provide the means to automatically switch between training and testing data The idea is to ... network to somehow store a record of the prior inputs and factor them in with the current data to produce an answer In recurrent networks, information about past inputs is fed back into and mixed ... can add some random noise to the neural network weights in order to try to break it free from the local minima The other option is to reset the network weights to new random values and start training...
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