final girma Aweke Thesis - Addis Ababa University Institutional
... I, the undersigned, declare that this thesis is my original work and has not been presented as a partial degree requirement for a degree in any other university and that all sources of materials used for the thesis have been duly acknowledged. ...
... I, the undersigned, declare that this thesis is my original work and has not been presented as a partial degree requirement for a degree in any other university and that all sources of materials used for the thesis have been duly acknowledged. ...
CLUEBOX: A Performance Log Analyzer for Automated Troubleshooting S. Ratna Sandeep
... could help an engineer rapidly identify the root-cause of the issue. In this section, we detail our learning methodology. 3.1.1 Reducing the Dimensionality of Data Clustering algorithms do not perform well if the number of features (counters) is large. Therefore, dimension reduction techniques are o ...
... could help an engineer rapidly identify the root-cause of the issue. In this section, we detail our learning methodology. 3.1.1 Reducing the Dimensionality of Data Clustering algorithms do not perform well if the number of features (counters) is large. Therefore, dimension reduction techniques are o ...
Data Mining Industry: Emerging Trends and New Opportunities
... there are many data mining companies that are trying to vertically integrate to offer the best services to broad markets. This is done by focusing on a particular industry and trying to understand the types of information collected by companies in that sector. Data mining is then the process of extr ...
... there are many data mining companies that are trying to vertically integrate to offer the best services to broad markets. This is done by focusing on a particular industry and trying to understand the types of information collected by companies in that sector. Data mining is then the process of extr ...
Customer Relationship Management (CRM) - E
... recording interactions and communications. Software solutions then expanded to embrace deal tracking, territories, opportunities, and at the sales pipeline itself. Next came the advent of tools for other client-interface business functions, as described below. These tools have been, and still are, o ...
... recording interactions and communications. Software solutions then expanded to embrace deal tracking, territories, opportunities, and at the sales pipeline itself. Next came the advent of tools for other client-interface business functions, as described below. These tools have been, and still are, o ...
Subgroup Discovery – Advanced Review
... In the binary, nominal and numeric setting a large number of quality functions has been proposed in literature, cf. (52; 66). In general, quality functions utilize the statistical distribution of the target concept(s) to score a subgroup pattern P . More complex quality functions compare a set of d ...
... In the binary, nominal and numeric setting a large number of quality functions has been proposed in literature, cf. (52; 66). In general, quality functions utilize the statistical distribution of the target concept(s) to score a subgroup pattern P . More complex quality functions compare a set of d ...
padhraic smyth - Donald Bren School of Information and Computer
... Dan Cooper, UC Irvine). 43. Scaling Statistical Topic Modeling Algorithms to Massive Data Sets, Yahoo! Faculty Research (FREP) award, $10,000 gift, May 2010, Principal Investigator. 42. Scalable Methods for the Analysis of Network-based Data, Office of Naval Research: Multidisciplinary University Re ...
... Dan Cooper, UC Irvine). 43. Scaling Statistical Topic Modeling Algorithms to Massive Data Sets, Yahoo! Faculty Research (FREP) award, $10,000 gift, May 2010, Principal Investigator. 42. Scalable Methods for the Analysis of Network-based Data, Office of Naval Research: Multidisciplinary University Re ...
Lecture 7: Outlier Detection
... ◦ Assumes that the normal data is generated by a parametric distribution with parameter θ ◦ The probability density function of the parametric distribution f(x, θ) gives the probability that object x is generated by the distribution ◦ The smaller this value, the more likely x is an outlier Non-param ...
... ◦ Assumes that the normal data is generated by a parametric distribution with parameter θ ◦ The probability density function of the parametric distribution f(x, θ) gives the probability that object x is generated by the distribution ◦ The smaller this value, the more likely x is an outlier Non-param ...
Frequent Itemset - delab-auth
... c(ABC D) c(AB CD) c(A BCD) Confidence is anti-monotone w.r.t. number of items on the RHS of the rule ...
... c(ABC D) c(AB CD) c(A BCD) Confidence is anti-monotone w.r.t. number of items on the RHS of the rule ...
1Introduction
... data should be more appropriately categorized as a machine learning system, a statistical data analysis tool, or an experimental system prototype. A system that can only perform data or information retrieval, including finding aggregate values, or that performs deductive query answering in large data ...
... data should be more appropriately categorized as a machine learning system, a statistical data analysis tool, or an experimental system prototype. A system that can only perform data or information retrieval, including finding aggregate values, or that performs deductive query answering in large data ...
Spatial Analysis Clustering
... neighborhood is called a core object • Object can only belong to a cluster if it is within the Epsilon neighborhood of at least one core object • Core object o within Epsilon neighborhood of another core object p must belong to the same cluster as p • Non-core object belonging to the Epsilon neighbo ...
... neighborhood is called a core object • Object can only belong to a cluster if it is within the Epsilon neighborhood of at least one core object • Core object o within Epsilon neighborhood of another core object p must belong to the same cluster as p • Non-core object belonging to the Epsilon neighbo ...
Data Mining within Eclipse - Department of Informatics
... In an ever so growing world of technology, where hardware becomes cheaper and better by the day, new opportunities arise, that were previously unimaginable or simply not feasible. The same applies to data mining. With the increasing possibility of storing information at a reasonable price wherever i ...
... In an ever so growing world of technology, where hardware becomes cheaper and better by the day, new opportunities arise, that were previously unimaginable or simply not feasible. The same applies to data mining. With the increasing possibility of storing information at a reasonable price wherever i ...
Dual Sentiment Analysis: Considering Two Sides of One Review
... Text reversion. If there is a negation, we first detect the scope of negation.2 All sentiment words out of the scope of negation are reversed to their antonyms. In the scope of negation, negation words (e.g., “no”, “not”, “don’t”, etc.) are removed, but the sentiment words are not reversed; Label re ...
... Text reversion. If there is a negation, we first detect the scope of negation.2 All sentiment words out of the scope of negation are reversed to their antonyms. In the scope of negation, negation words (e.g., “no”, “not”, “don’t”, etc.) are removed, but the sentiment words are not reversed; Label re ...
Title of slide - Royal Holloway, University of London
... The PDG needs pragmatic solutions for averages where the reported information may be incomplete/inconsistent. Often this involves taking the quadratic sum of statistical and systematic uncertainties for LS averages. If asymmetric errors (confidence intervals) are reported, PDG has a recipe to recons ...
... The PDG needs pragmatic solutions for averages where the reported information may be incomplete/inconsistent. Often this involves taking the quadratic sum of statistical and systematic uncertainties for LS averages. If asymmetric errors (confidence intervals) are reported, PDG has a recipe to recons ...
Chapter 6 A SURVEY OF TEXT CLASSIFICATION
... class-distribution. Thus, the features found by LSI are not necessarily the directions along which the class-distribution of the underlying documents can be best separated. A modest level of success has been obtained in improving classification accuracy by using boosting techniques in conjunction wit ...
... class-distribution. Thus, the features found by LSI are not necessarily the directions along which the class-distribution of the underlying documents can be best separated. A modest level of success has been obtained in improving classification accuracy by using boosting techniques in conjunction wit ...
Map-centred exploratory approach to multiple
... decision models, we speculate that better integration of maps and MCDM tools through data visualization can also become a new structure improving the understanding of decision situations, and consequently leading to better outcomes of the decision making process. We propose that such integration be ...
... decision models, we speculate that better integration of maps and MCDM tools through data visualization can also become a new structure improving the understanding of decision situations, and consequently leading to better outcomes of the decision making process. We propose that such integration be ...
Contents - Emory Math/CS Department
... According to William H. Inmon, a leading architect in the construction of data warehouse systems, “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision making process” [Inm96]. This short, but comprehensive definitio ...
... According to William H. Inmon, a leading architect in the construction of data warehouse systems, “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision making process” [Inm96]. This short, but comprehensive definitio ...
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.