Open-Source Machine Learning: R Meets Weka
... Discretize()), data available as an R data frame are transformed to Weka instances, filtered, and transformed back to a data frame. If the next data analysis step again employs an RWeka interface function, some of these data transformations are unnecessary. A natural idea would be having common R/Ja ...
... Discretize()), data available as an R data frame are transformed to Weka instances, filtered, and transformed back to a data frame. If the next data analysis step again employs an RWeka interface function, some of these data transformations are unnecessary. A natural idea would be having common R/Ja ...
Rule-Based Classifier
... – If k is too small, sensitive to noise points – If k is too large, neighborhood may include points from other classes ...
... – If k is too small, sensitive to noise points – If k is too large, neighborhood may include points from other classes ...
Class Imbalance problem in Fraud Detection.
... DEGREE OF M.SC IN COMPUTING AT INSTITUTE OF TECHNOLOGY BLANCHARDSTOWN DUBLIN, IRELAND ...
... DEGREE OF M.SC IN COMPUTING AT INSTITUTE OF TECHNOLOGY BLANCHARDSTOWN DUBLIN, IRELAND ...
as a PDF
... the possibility of applying graph and lattice theory to compare clustering results. However, enumerating all the possible clusterings is a hard task for clustering large datasets. Due to the lack of meaningful pair-wise distance function, entropy-based measure has been widely used as a generic measu ...
... the possibility of applying graph and lattice theory to compare clustering results. However, enumerating all the possible clusterings is a hard task for clustering large datasets. Due to the lack of meaningful pair-wise distance function, entropy-based measure has been widely used as a generic measu ...
Temporal Patterns Discovery from Multivariate Time Series via
... intervals, possibly at a higher level of abstraction, which are either a part of the original raw input data (e.g., administration of a medication for 4 days), or are abstractions, or interpretations, derived from them (e.g., two weeks of moderate anemia or of Grade II liver dysfunction). Figure 1 i ...
... intervals, possibly at a higher level of abstraction, which are either a part of the original raw input data (e.g., administration of a medication for 4 days), or are abstractions, or interpretations, derived from them (e.g., two weeks of moderate anemia or of Grade II liver dysfunction). Figure 1 i ...
Fast Algorithms for Sequential Pattern Mining
... mining will be introduced. Thus, effective and efficient sequential pattern mining is an important and interesting research problem. Efficient sequential pattern mining methodologies have been studied extensively in many related problems, including the general sequential pattern mining [5, 7, 79, 8 ...
... mining will be introduced. Thus, effective and efficient sequential pattern mining is an important and interesting research problem. Efficient sequential pattern mining methodologies have been studied extensively in many related problems, including the general sequential pattern mining [5, 7, 79, 8 ...
yes - 淡江大學
... – use model to predict continuous or ordered value for a given input • Prediction is different from classification – Classification refers to predict categorical class label – Prediction models continuous-valued functions • Major method for prediction: regression – model the relationship between one ...
... – use model to predict continuous or ordered value for a given input • Prediction is different from classification – Classification refers to predict categorical class label – Prediction models continuous-valued functions • Major method for prediction: regression – model the relationship between one ...
Data Mining Lab Manual - MLR Institute of Technology
... 7. Check to see if the data shows a bias against "foreign workers" (attribute 20),or "personal-status" (attribute 9). One way to do this (perhaps rather simple minded) is to remove these attributes from the dataset and see if the decision tree created in those cases is significantly different from t ...
... 7. Check to see if the data shows a bias against "foreign workers" (attribute 20),or "personal-status" (attribute 9). One way to do this (perhaps rather simple minded) is to remove these attributes from the dataset and see if the decision tree created in those cases is significantly different from t ...
Oracle Data Mining Concepts
... The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this software or related documentation is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Governme ...
... The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this software or related documentation is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Governme ...
Spatio-Temporal Data Mining with Event Logs from High Volume
... I would like to express my sincere gratitude to my advisor Prof. João Sousa for the continuous support during my MSc study and related research. His guidance helped me in time of research and writing of this thesis. Besides my advisor, I would like to thank Prof. Rui Jorge de Almeida for his insigh ...
... I would like to express my sincere gratitude to my advisor Prof. João Sousa for the continuous support during my MSc study and related research. His guidance helped me in time of research and writing of this thesis. Besides my advisor, I would like to thank Prof. Rui Jorge de Almeida for his insigh ...
Ensembles for Unsupervised Outlier Detection: Challenges
... of two randomly chosen objects, one positive example (outlier) and one negative example (inlier), is sorted correctly (i.e., the outlier is ranked before the inlier) [29]. ROC curves and ROC AUC analysis inherently treat the class imbalance problem by using the relative frequencies which makes them ...
... of two randomly chosen objects, one positive example (outlier) and one negative example (inlier), is sorted correctly (i.e., the outlier is ranked before the inlier) [29]. ROC curves and ROC AUC analysis inherently treat the class imbalance problem by using the relative frequencies which makes them ...
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.