- Courses - University of California, Berkeley
... the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools. ...
... the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools. ...
Predictive Analytics, Data Mining and Big Data
... The author has asserted his right to be identified as the author of this work in accordance with the Copyright, Designs and Patents Act 1988. ...
... The author has asserted his right to be identified as the author of this work in accordance with the Copyright, Designs and Patents Act 1988. ...
On the Difficulty of Nearest Neighbor Search
... is that as s increases (denser vectors), contrast decreases, making nearest neighbor search harder. In other words, lesser the number of non-zero dimensions for a fixed d, easier the search. In fact, the search remains well-behaved even in high-dimensional datasets if data is sparse. The prediction ...
... is that as s increases (denser vectors), contrast decreases, making nearest neighbor search harder. In other words, lesser the number of non-zero dimensions for a fixed d, easier the search. In fact, the search remains well-behaved even in high-dimensional datasets if data is sparse. The prediction ...
Association Rules and Predictive Models for e
... the left side of the chart. In many cases, when comparing multiple models lines cross, so that one model stands higher in one part of the chart while another is elevated higher than the first in a different part of the chart. In this case, it is necessary to consider which portion of the sample is d ...
... the left side of the chart. In many cases, when comparing multiple models lines cross, so that one model stands higher in one part of the chart while another is elevated higher than the first in a different part of the chart. In this case, it is necessary to consider which portion of the sample is d ...
Data Stream Mining
... • Data streams are dynamic and infinite in size – Data is continuously generated and changing – Live streams may have no upper limit – A live stream, can be read only once (“Just One ...
... • Data streams are dynamic and infinite in size – Data is continuously generated and changing – Live streams may have no upper limit – A live stream, can be read only once (“Just One ...
Large-Scale Unusual Time Series Detection
... explaining the variance in different scenarios. Experiments of the method are described in Section 4. Related work and conclusions are presented in Sections 5 and 6 respectively. ...
... explaining the variance in different scenarios. Experiments of the method are described in Section 4. Related work and conclusions are presented in Sections 5 and 6 respectively. ...
No Slide Title
... modeling techniques is based upon the data mining objective – Modeling is an iterative process different for supervised and unsupervised learning • May model for either description or prediction CS590D ...
... modeling techniques is based upon the data mining objective – Modeling is an iterative process different for supervised and unsupervised learning • May model for either description or prediction CS590D ...
View/Download-PDF - International Journal of Computer Science
... ZeroR:- ZeroR is the simplest classification method which relies on the target and ignores all predictors. ZeroR classifier simply predicts the majority category (class). Although there is no predictability power in ZeroR, it is useful for determining a baseline performance as a benchmark for other ...
... ZeroR:- ZeroR is the simplest classification method which relies on the target and ignores all predictors. ZeroR classifier simply predicts the majority category (class). Although there is no predictability power in ZeroR, it is useful for determining a baseline performance as a benchmark for other ...
Wild Life Protection by Moving Object Data Mining
... cluster of a universe. These granules are composed of finer granules that are drawn together by distinguishability, similarity, and functionality. A group of concepts or objects can be considered as a granule by their spatial neighbourhood, closeness, and cohesion. Although granular computing is int ...
... cluster of a universe. These granules are composed of finer granules that are drawn together by distinguishability, similarity, and functionality. A group of concepts or objects can be considered as a granule by their spatial neighbourhood, closeness, and cohesion. Although granular computing is int ...
Big Data or Right Data?
... where β > 1 is a constant, the number of correct entities found by the faster algorithm will be larger. For some cases this will imply big data, but for many other cases it will not (for example, if the better quality algorithm has quadratic time performance). Another important aspect of scalability ...
... where β > 1 is a constant, the number of correct entities found by the faster algorithm will be larger. For some cases this will imply big data, but for many other cases it will not (for example, if the better quality algorithm has quadratic time performance). Another important aspect of scalability ...
Mining and Summarizing Customer Reviews
... Statute of limitations: No grading questions or complaints, no matter how justified, will be listened to one week after the item in question has been returned. Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' wo ...
... Statute of limitations: No grading questions or complaints, no matter how justified, will be listened to one week after the item in question has been returned. Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' wo ...
Things about Trace Analysis
... Q3. Similarity Metrics between Users • Take users in the same clusters and concatenate the asso. matrices, and perform SVD and find power captured by top k eigen vectors. • Also take random users and concatenate the eigenvectors and do the same. • There is a clear distinction between the 2 clusteri ...
... Q3. Similarity Metrics between Users • Take users in the same clusters and concatenate the asso. matrices, and perform SVD and find power captured by top k eigen vectors. • Also take random users and concatenate the eigenvectors and do the same. • There is a clear distinction between the 2 clusteri ...
research papers
... Nowadays, exploring and analyzing medical data is a very topical issue, because it is often stored in a way, in which it cannot be easily analyzed. It is common that this data is also usually of a very low quality. This is why some preprocessing techniques should be used to enhance the quality of th ...
... Nowadays, exploring and analyzing medical data is a very topical issue, because it is often stored in a way, in which it cannot be easily analyzed. It is common that this data is also usually of a very low quality. This is why some preprocessing techniques should be used to enhance the quality of th ...
Chapter 4 Describing the Relation Between Two Variables
... The following data are based on a study for drilling rock. The researchers wanted to determine whether the time it takes to dry drill a distance of 5 feet in rock increases with the depth at which the drilling begins. So, depth at which drilling begins is the predictor variable, x, and time (in minu ...
... The following data are based on a study for drilling rock. The researchers wanted to determine whether the time it takes to dry drill a distance of 5 feet in rock increases with the depth at which the drilling begins. So, depth at which drilling begins is the predictor variable, x, and time (in minu ...
The Art and Technology of Data Mining
... including 1257 without particles and 91 with particles Dataset 2 with sharp and blurry images: 2000 images including 1909 without particles and blurry particles and 91 with particles 54 Input variables, all numeric ...
... including 1257 without particles and 91 with particles Dataset 2 with sharp and blurry images: 2000 images including 1909 without particles and blurry particles and 91 with particles 54 Input variables, all numeric ...
An Interactive Data Repository with Visual Analytics
... correlation between pairs of node/link statistics (see an example in Figure 3), which supports brushing to allow users to highlight interesting nodes (and links) across the various measures. Furthermore, semantic zooming can be used to drill-down in order to understand the di↵erences between individ ...
... correlation between pairs of node/link statistics (see an example in Figure 3), which supports brushing to allow users to highlight interesting nodes (and links) across the various measures. Furthermore, semantic zooming can be used to drill-down in order to understand the di↵erences between individ ...
Data Mining Revision Controlled Document History Metadata for
... calculated and considered as a point. Then the next two closest points (or clusters) are combined to form a new cluster. This process is repeated until all the points and clusters are merged into a single cluster. The resulting pairings can then be drawn out to create a hierarchical tree showing the ...
... calculated and considered as a point. Then the next two closest points (or clusters) are combined to form a new cluster. This process is repeated until all the points and clusters are merged into a single cluster. The resulting pairings can then be drawn out to create a hierarchical tree showing the ...
An Interactive Data Repository with Visual Analytics
... correlation between pairs of node/link statistics (see an example in Figure 3), which supports brushing to allow users to highlight interesting nodes (and links) across the various measures. Furthermore, semantic zooming can be used to drill-down in order to understand the di↵erences between individ ...
... correlation between pairs of node/link statistics (see an example in Figure 3), which supports brushing to allow users to highlight interesting nodes (and links) across the various measures. Furthermore, semantic zooming can be used to drill-down in order to understand the di↵erences between individ ...
NETWORK INTRUSION DETECTION BASED ON ROUGH SET AND
... the number of tuples of Ci in D1 by |D1|, the total number of tuples in D1. In selecting a spilt-point for attribute A, pick an attribute value that gives the minimum information required. This process is performed recursively on an attribute until the information requirement is less than a small th ...
... the number of tuples of Ci in D1 by |D1|, the total number of tuples in D1. In selecting a spilt-point for attribute A, pick an attribute value that gives the minimum information required. This process is performed recursively on an attribute until the information requirement is less than a small th ...
Introduction
... Statute of limitations: No grading questions or complaints, no matter how justified, will be listened to one week after the item in question has been returned. Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' wo ...
... Statute of limitations: No grading questions or complaints, no matter how justified, will be listened to one week after the item in question has been returned. Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' wo ...
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.