scaling up classification rule induction through parallel processing
... CLASSIFICATION RULE INDUCTION ALGORITHMS • Step 1: Each processor induces rule terms ‘locally’ on attribute lists it holds in memory by calculating all the conditional probabilities for the target class in all the attribute lists and taking the largest one. If there is a tie break, the rule term tha ...
... CLASSIFICATION RULE INDUCTION ALGORITHMS • Step 1: Each processor induces rule terms ‘locally’ on attribute lists it holds in memory by calculating all the conditional probabilities for the target class in all the attribute lists and taking the largest one. If there is a tie break, the rule term tha ...
Clustering, Dimensionality Reduction, and Side
... There are so many people who have been so kind and so helpful to me during all these years; all of you have made a mark in my life! First and foremost, I want to express my greatest gratitude to my thesis supervisor Dr. Anil Jain. He is such a wonderful advisor, mentor, and motivator. Under his guid ...
... There are so many people who have been so kind and so helpful to me during all these years; all of you have made a mark in my life! First and foremost, I want to express my greatest gratitude to my thesis supervisor Dr. Anil Jain. He is such a wonderful advisor, mentor, and motivator. Under his guid ...
Załącznik nr 6 do ZW 15/2007
... Application examples shown here are based on samples from real life datasets, are formulated based on real life problems, and are implemented using practical tools: SAS Enterprise Miner software for the data mining part, and MS SQL Server Integration Services and Analysis Services for the data wareh ...
... Application examples shown here are based on samples from real life datasets, are formulated based on real life problems, and are implemented using practical tools: SAS Enterprise Miner software for the data mining part, and MS SQL Server Integration Services and Analysis Services for the data wareh ...
Data Mining
... Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, patternbased classification, logistic regression, … ...
... Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, patternbased classification, logistic regression, … ...
Clustering - Computer Science
... Hierarchical Clustering • Produces a set of nested clusters organized as a hierarchical tree • Can be visualized as a dendrogram – A tree like diagram that records the sequences of merges or splits ...
... Hierarchical Clustering • Produces a set of nested clusters organized as a hierarchical tree • Can be visualized as a dendrogram – A tree like diagram that records the sequences of merges or splits ...
Arguing From Experience to Classifying Noisy Data
... mistakes. In certain domains, such as welfare benefits, it is estimated that 30% or more of previous examples may have been wrongly classified [18]. Any classifier relying on such data must therefore be robust in the face of quite high levels of noise. Conceptually example cases are presented for cl ...
... mistakes. In certain domains, such as welfare benefits, it is estimated that 30% or more of previous examples may have been wrongly classified [18]. Any classifier relying on such data must therefore be robust in the face of quite high levels of noise. Conceptually example cases are presented for cl ...
Data Mining - Universität Wien
... • OLAP – analysis techniques with functionlities such as summarization, consolidation, and aggregation, as well as the ability to view information from different angles. • Data mining – extracting or “mining“ knowledge from large data sets. • Text mining – “mining“ large textual (document) databases ...
... • OLAP – analysis techniques with functionlities such as summarization, consolidation, and aggregation, as well as the ability to view information from different angles. • Data mining – extracting or “mining“ knowledge from large data sets. • Text mining – “mining“ large textual (document) databases ...
CENG 464 Introduction to Data Mining Getting to Know Your Data
... – Gain insight into an information space by mapping data onto graphical primitives – Provide qualitative overview of large data sets – Search for patterns, trends, structure, irregularities, relationships among data – Help find interesting regions and suitable parameters for further quantitative ana ...
... – Gain insight into an information space by mapping data onto graphical primitives – Provide qualitative overview of large data sets – Search for patterns, trends, structure, irregularities, relationships among data – Help find interesting regions and suitable parameters for further quantitative ana ...
CS490D: Introduction to Data Mining Chris Clifton What Is Data
... Safety Board (NTSB) and the Federal Aviation Administration (FAA) • Integrating data from different sources as well as mining for patterns from a mix of both structured fields and free text is a difficult task • The goal of our initial analysis is to determine how data mining can be used to improve ...
... Safety Board (NTSB) and the Federal Aviation Administration (FAA) • Integrating data from different sources as well as mining for patterns from a mix of both structured fields and free text is a difficult task • The goal of our initial analysis is to determine how data mining can be used to improve ...
1 Aggregating and visualizing a single feature: 1D analysis
... We are going to be concerned with presenting data as maps or diagrams or objects on a digital screen in such a way that relations between data entities or features are reflected in distances or connections, or other visual relations, between their images. Among more or less distinct visualization go ...
... We are going to be concerned with presenting data as maps or diagrams or objects on a digital screen in such a way that relations between data entities or features are reflected in distances or connections, or other visual relations, between their images. Among more or less distinct visualization go ...
Efficient Data Mining Algorithm for Reducing High Toll Transactions
... Utility pattern growth by pushing two more strategies into the framework of FPGrowth. By the strategies, overestimated utilities of item sets can be decreased and thus the number of PHUIs can be further reduced. To address this issue, we propose two novel algorithms as well as a compact data structu ...
... Utility pattern growth by pushing two more strategies into the framework of FPGrowth. By the strategies, overestimated utilities of item sets can be decreased and thus the number of PHUIs can be further reduced. To address this issue, we propose two novel algorithms as well as a compact data structu ...
ESDA jul2016 Session Presentation
... logarithm of its expected value can be modeled by a linear combination of unknown parameters Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of Principal Component Analys ...
... logarithm of its expected value can be modeled by a linear combination of unknown parameters Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of Principal Component Analys ...
Towards an Open Service Architecture for Data Mining
... Grid Services, is already available. As was already mentioned, Grid computing began with an emphasis on compute-intensive tasks, which benefit from massive parallelism for their computation needs, but are not data intensive; the data that they operate on does not scale in portion to the computation ...
... Grid Services, is already available. As was already mentioned, Grid computing began with an emphasis on compute-intensive tasks, which benefit from massive parallelism for their computation needs, but are not data intensive; the data that they operate on does not scale in portion to the computation ...
An Efficient Hierarchical Clustering Algorithm for Large Datasets
... there exists a significant need to develop a hierarchical clustering algorithm for large datasets. Approximating hierarchical clustering in subquadratic time and memory has been previously attempted [14-18]. However, these methods either rely on embedding into spaces that are not biologically sensib ...
... there exists a significant need to develop a hierarchical clustering algorithm for large datasets. Approximating hierarchical clustering in subquadratic time and memory has been previously attempted [14-18]. However, these methods either rely on embedding into spaces that are not biologically sensib ...
Mining Clinical Data with a Temporal Dimension: a Case Study
... symptoms, drug assumptions and reactions to treatments are a key information. In all these cases, enforcing fixed time constraints on the mined sequences is not a solution. It is desirable that typical transition times, when they exist, emerge from the input data. TAS patterns have been also used as ...
... symptoms, drug assumptions and reactions to treatments are a key information. In all these cases, enforcing fixed time constraints on the mined sequences is not a solution. It is desirable that typical transition times, when they exist, emerge from the input data. TAS patterns have been also used as ...
Stock Market Prediction using Social Media Analysis
... The correlation between two events do not necessarily imply that one of the events have caused the other. Causality is defined as that the events have caused each other. In statistics, pre-existing data or experimental data is employed to infer causality by regression methods. When analyzing a casua ...
... The correlation between two events do not necessarily imply that one of the events have caused the other. Causality is defined as that the events have caused each other. In statistics, pre-existing data or experimental data is employed to infer causality by regression methods. When analyzing a casua ...
Validation of an Association Rule Mining
... To find associations between medications and problems at UT, we employed association rule mining, a technique which is widely used in computer science, data mining and electronic commerce [20-22]. Association rule mining assumes a database of “items” and a set of “transactions”. In the commerce scen ...
... To find associations between medications and problems at UT, we employed association rule mining, a technique which is widely used in computer science, data mining and electronic commerce [20-22]. Association rule mining assumes a database of “items” and a set of “transactions”. In the commerce scen ...
Association of Data Mining and Healthcare Domain: Issues and
... The demand and want for data mining is more in field of healthcare, regardless of variations and conflicts in processes. Various discussions led to the demand of data mining in the field of healthcare which includes both public health as well private health. Many facts can be achieved from the past ...
... The demand and want for data mining is more in field of healthcare, regardless of variations and conflicts in processes. Various discussions led to the demand of data mining in the field of healthcare which includes both public health as well private health. Many facts can be achieved from the past ...
Do People Still Miss Steve Jobs As the CEO of Apple Inc.? A Text Mining Approach: Comparing SAS® and R
... technique in SAS and R. „Get tweet‟ macro is used to fetch data from twitter in SAS while „twitteR‟ package is used to fetch data from twitter in R. SAS Text Miner was used in SAS to analyze the data while „tm‟ package was used to analyze the data in R. ...
... technique in SAS and R. „Get tweet‟ macro is used to fetch data from twitter in SAS while „twitteR‟ package is used to fetch data from twitter in R. SAS Text Miner was used in SAS to analyze the data while „tm‟ package was used to analyze the data in R. ...
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.