Chapter 7
... Remove outliers (e.g. 10% of points farthest from the regression plane) Minimize median instead of mean of squares (copes with outliers in x and y direction) ...
... Remove outliers (e.g. 10% of points farthest from the regression plane) Minimize median instead of mean of squares (copes with outliers in x and y direction) ...
Clustering of Streaming Time Series is Meaningless
... • having proven meaningless of k-means clustering of STS, the experiment has been performed using hierarchical clustering • new challenge: defining distance between two clusters: linkage method - applicable for bottom-up clustering cluster objects can be based on different methods: Single Linkage: t ...
... • having proven meaningless of k-means clustering of STS, the experiment has been performed using hierarchical clustering • new challenge: defining distance between two clusters: linkage method - applicable for bottom-up clustering cluster objects can be based on different methods: Single Linkage: t ...
Data Mining
... Database systems are being used since the 1960s in the Western countries (perhaps since 1980s in India). These systems have generated mountains of data. Point of sale terminals and bar codes on many ...
... Database systems are being used since the 1960s in the Western countries (perhaps since 1980s in India). These systems have generated mountains of data. Point of sale terminals and bar codes on many ...
M.Tech ICT - Punjabi University
... error, estimation of error rates, characteristic curves, estimating the composition of populations, introduction to Supervised Parametric Approaches and Unsupervised Approaches. Cluster analysis: clustering techniques, cluster analysis, cluster validity. Feature selection & extraction: feature selec ...
... error, estimation of error rates, characteristic curves, estimating the composition of populations, introduction to Supervised Parametric Approaches and Unsupervised Approaches. Cluster analysis: clustering techniques, cluster analysis, cluster validity. Feature selection & extraction: feature selec ...
A Top-Down Row Enumeration Approach of Frequent Patterns from
... A specific kind of high dimensional data set, which contain as and a large number of tuples. many as tens of thousands of columns but only a hundred or a thousand rows, such as microarray data. Different from transactional data set, which usually have a small number of columns and a large number of ...
... A specific kind of high dimensional data set, which contain as and a large number of tuples. many as tens of thousands of columns but only a hundred or a thousand rows, such as microarray data. Different from transactional data set, which usually have a small number of columns and a large number of ...
The Early Warning Project: Prewarning and Avoiding Problems and Costly Downtime in Complex Industrial Processes
... experienced problems. Which kind of knowledge may be extracted from this? This way of processing the data has the benefit that it is possible to assign interpretations to the clusters, using knowledge from the operators and process experts about what actually happened on those particular times. Cert ...
... experienced problems. Which kind of knowledge may be extracted from this? This way of processing the data has the benefit that it is possible to assign interpretations to the clusters, using knowledge from the operators and process experts about what actually happened on those particular times. Cert ...
Big Data Clustering
... can keep all the data points within each cluster, whose computational as well as space cost might be too high for large data sets. To address this issue, clustering using representatives (CURE) [22] proposes to use a set of well-scattered data points to represent a cluster. CURE is essentially a hie ...
... can keep all the data points within each cluster, whose computational as well as space cost might be too high for large data sets. To address this issue, clustering using representatives (CURE) [22] proposes to use a set of well-scattered data points to represent a cluster. CURE is essentially a hie ...
part i – create a data mining project
... A discrete variable is one with a well-defined finite set of possible values while a continuous variable is one which can take on a value between any other two values. 1. On the Specify Columns' Content and Data Type page, click Detect to run an algorithm that determines the default data and content ...
... A discrete variable is one with a well-defined finite set of possible values while a continuous variable is one which can take on a value between any other two values. 1. On the Specify Columns' Content and Data Type page, click Detect to run an algorithm that determines the default data and content ...
Integration of Data Mining in Cloud Computing
... Change and deviation detection focuses on discovering the most significant changes in the data from previously measured values. Dependency modeling consists of finding a model that describes significant dependencies between variables. Dependency models exist at two levels: (1) the structural lev ...
... Change and deviation detection focuses on discovering the most significant changes in the data from previously measured values. Dependency modeling consists of finding a model that describes significant dependencies between variables. Dependency models exist at two levels: (1) the structural lev ...
Improving Decision Tree Performance by Exception Handling
... The count of the distinct value is 2 in Table 3. Hence, two rules should be generated from the table but only one rule is generated with the help of majority voting and the other rule cannot be generated If Ak = V1k then C = ? The value for Ak can be found by majority voting as Ak = V1k but its corr ...
... The count of the distinct value is 2 in Table 3. Hence, two rules should be generated from the table but only one rule is generated with the help of majority voting and the other rule cannot be generated If Ak = V1k then C = ? The value for Ak can be found by majority voting as Ak = V1k but its corr ...
Accurate and Large-Scale Privacy-Preserving Data Mining using the
... Data mining technologies, aiming at extracting valuable, non obvious information from large quantities of data [2], have broad applications in areas related to market research, as well as to financial and scientific research. Despite the potentials for offering valuable services, there have been con ...
... Data mining technologies, aiming at extracting valuable, non obvious information from large quantities of data [2], have broad applications in areas related to market research, as well as to financial and scientific research. Despite the potentials for offering valuable services, there have been con ...
DefenseStanley - Department of Computing Science
... • Communication cost is of the order O(mkl), where l represents the size (in bits) required to transmit a dataset from one party to a central or third party. • Conclusion The DRBT is effective for PPC over centralized data and vertically partitioned data. ...
... • Communication cost is of the order O(mkl), where l represents the size (in bits) required to transmit a dataset from one party to a central or third party. • Conclusion The DRBT is effective for PPC over centralized data and vertically partitioned data. ...
Top Ten Big Data Security and Privacy Challenges
... The term big data refers to the massive amounts of digital information companies and governments collect about us and our surroundings. Every day, we create 2.5 quintillion bytes of data—so much that 90% of the data in the world today has been created in the last two years alone. Security and privac ...
... The term big data refers to the massive amounts of digital information companies and governments collect about us and our surroundings. Every day, we create 2.5 quintillion bytes of data—so much that 90% of the data in the world today has been created in the last two years alone. Security and privac ...
Using consumer behavior data to reduce energy
... algorithms, as well as genetic algorithms, from the family of heuristic algorithms, are suitable for finding frequent patterns in large datasets. In this work, we consider only deterministic algorithm, since they are able to find patterns in a reasonable amount of time and do not have the disadvanta ...
... algorithms, as well as genetic algorithms, from the family of heuristic algorithms, are suitable for finding frequent patterns in large datasets. In this work, we consider only deterministic algorithm, since they are able to find patterns in a reasonable amount of time and do not have the disadvanta ...
Amendment No.1 - 28th ACM - 23 March 2015
... Prerequisite Knowledge of C and C++ and Core Java Programming ...
... Prerequisite Knowledge of C and C++ and Core Java Programming ...
Proposed Syllabus for M.Sc. (Computer Science)
... It is assumed that student learning this course have the following background: • Experience with an OOP language (such as Java or C++) • Experience with a procedural language (such as C) • Working knowledge of C, C++, and Java programming. • Basic algorithms and data structure concepts. Why to study ...
... It is assumed that student learning this course have the following background: • Experience with an OOP language (such as Java or C++) • Experience with a procedural language (such as C) • Working knowledge of C, C++, and Java programming. • Basic algorithms and data structure concepts. Why to study ...
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.