Day 9 = Tuesday 7/24/2007
... Sample Midterm Question #5: If a data set is space delimited, what should be done to allow a text string that includes a space so that R or Excel will not split the string into 2 columns? A) Escape it B) Remove the space C) Use all capitals in the string D) Select “Fix the spaces” from the menu bar ...
... Sample Midterm Question #5: If a data set is space delimited, what should be done to allow a text string that includes a space so that R or Excel will not split the string into 2 columns? A) Escape it B) Remove the space C) Use all capitals in the string D) Select “Fix the spaces” from the menu bar ...
Rules Extraction and Representation for Geographic
... obtained from different sources and in different time frames. It is, therefore, important for the users to understand the nature of available data in order to use them appropriately for a specific task. In this paper we investigate knowledge representation and sharing in Geographic Information Syste ...
... obtained from different sources and in different time frames. It is, therefore, important for the users to understand the nature of available data in order to use them appropriately for a specific task. In this paper we investigate knowledge representation and sharing in Geographic Information Syste ...
GhostMiner Wine example
... Optimize k, the number of neighbors included. Optimize the scaling factors of features Wi|Xi-Yi|: this goes beyond feature selection. Use search-based techniques to find good scaling parameters for features. Notice that: For k=1 always 100% on the training set is obtained! To evaluate accuracy on tr ...
... Optimize k, the number of neighbors included. Optimize the scaling factors of features Wi|Xi-Yi|: this goes beyond feature selection. Use search-based techniques to find good scaling parameters for features. Notice that: For k=1 always 100% on the training set is obtained! To evaluate accuracy on tr ...
Big speech data analytics for contact centers
... generates 2,000 hours of audio every 24 hours. This represents a wealth of information for both CC operators and clients, however, only 1-3% of all calls are audited by supervisors nowadays, or analyzed by basic automatic keyword spotting with no direct link between such call analysis and business s ...
... generates 2,000 hours of audio every 24 hours. This represents a wealth of information for both CC operators and clients, however, only 1-3% of all calls are audited by supervisors nowadays, or analyzed by basic automatic keyword spotting with no direct link between such call analysis and business s ...
Chapter 1 Business Driven Technology
... • Forecast the cost of moving military equipment • Testing strategies for potential military engagements • Capture data on where customers are flying and the ultimate destination of passengers who change airlines in hub cities: is there a new route that should be added? • Predict what type of show i ...
... • Forecast the cost of moving military equipment • Testing strategies for potential military engagements • Capture data on where customers are flying and the ultimate destination of passengers who change airlines in hub cities: is there a new route that should be added? • Predict what type of show i ...
DIMACS Working Group on Data Mining and Epidemiology
... 1. Streaming Data Analysis: •When you only have one shot at the data •Widely used to detect trends and sound alarms in applications in telecommunications and finance •AT&T uses this to detect fraudulent use of credit cards or impending billing defaults •Columbia has developed methods for detecting ...
... 1. Streaming Data Analysis: •When you only have one shot at the data •Widely used to detect trends and sound alarms in applications in telecommunications and finance •AT&T uses this to detect fraudulent use of credit cards or impending billing defaults •Columbia has developed methods for detecting ...
Special Session on “Application of Nature Inspired Algorithms for
... determined. Hence, feature selection methods helps discovering these critical variables. This session is devoted to novel research featuring feature selection and function approximation in real world application problems. ...
... determined. Hence, feature selection methods helps discovering these critical variables. This session is devoted to novel research featuring feature selection and function approximation in real world application problems. ...
2013_COMP5318_Lectur..
... Suppose want to compute similarity between two vectors: x = <3,4,1,2>; y =<1,2,3,1> Step 1: compute the length of each vector: ||x||= (32 + 42 + 12 + 22 )1/2 = (9 + 16 + 1+4)1/2 = 5.48 ||y|| = (12 + 22 + 32+ 12 )1/2= (1 + 4 + 9 + 1)1/2 = 3.87 Step 2: compute the dot product: x.y = 3.1 + 4.2 + 1.3+ 2 ...
... Suppose want to compute similarity between two vectors: x = <3,4,1,2>; y =<1,2,3,1> Step 1: compute the length of each vector: ||x||= (32 + 42 + 12 + 22 )1/2 = (9 + 16 + 1+4)1/2 = 5.48 ||y|| = (12 + 22 + 32+ 12 )1/2= (1 + 4 + 9 + 1)1/2 = 3.87 Step 2: compute the dot product: x.y = 3.1 + 4.2 + 1.3+ 2 ...
OLAP Technology
... Share the cost of sorting among multiple group bys that need it Applies only to sort-based methods Extensive use of pipelining If you sort in order “ABC”, then the result is also sorted in order “AB” ...
... Share the cost of sorting among multiple group bys that need it Applies only to sort-based methods Extensive use of pipelining If you sort in order “ABC”, then the result is also sorted in order “AB” ...
Slide 1
... • Data points are represented as sets (i.e., sets of shingles) • Similar data points have large intersections in ...
... • Data points are represented as sets (i.e., sets of shingles) • Similar data points have large intersections in ...
Document
... • Data points are represented as sets (i.e., sets of shingles) • Similar data points have large intersections in ...
... • Data points are represented as sets (i.e., sets of shingles) • Similar data points have large intersections in ...
khawar-shakeel-university-of-gujrat-pakistan
... The data of courses having higher percentage of low grades are selected for analysis, For the selected courses, the data of all students is collected. ...
... The data of courses having higher percentage of low grades are selected for analysis, For the selected courses, the data of all students is collected. ...
1. A density grid-based clustering algorithm for uncertain data streams
... Abstract: This paper proposes a grid-based clustering algorithm Clu-US which is competent to find clusters of nonconvex shapes on uncertain data stream. Clu-US maps the uncertain data tuples to the grid space which could store and update the summary information of stream. The uncertainty of data is ...
... Abstract: This paper proposes a grid-based clustering algorithm Clu-US which is competent to find clusters of nonconvex shapes on uncertain data stream. Clu-US maps the uncertain data tuples to the grid space which could store and update the summary information of stream. The uncertainty of data is ...
File
... customer attrition or churn, is a critical factor for many companies operating in service industries. Suppose that the marketing manager can rely on a budget adequate to pursue a customer retention campaign aimed at 2000 individuals out of a total customer base of 2 million people. Hence, the questi ...
... customer attrition or churn, is a critical factor for many companies operating in service industries. Suppose that the marketing manager can rely on a budget adequate to pursue a customer retention campaign aimed at 2000 individuals out of a total customer base of 2 million people. Hence, the questi ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.