G070840-00 - DCC
... Welle, Q-scan, BN event display, Multidimensional classification, NoiseFloorMon etc. have been in use to explain the source of the glitches. Trigger clustering is an important step towards identification of distinct sources of triggers and unknown pattern discovery. This work is supported by NSF CRE ...
... Welle, Q-scan, BN event display, Multidimensional classification, NoiseFloorMon etc. have been in use to explain the source of the glitches. Trigger clustering is an important step towards identification of distinct sources of triggers and unknown pattern discovery. This work is supported by NSF CRE ...
Pre-Processing Structured Data for Standard Machine Learning
... typically generates a small number of sub-graphs that best compress the dataset. We have chosen SUBDUE as a reference method, since it is publicly available and the sub-graphs discovered by SUBDUE could be used as (propositional) features for standard machine learning algorithms. We have used the im ...
... typically generates a small number of sub-graphs that best compress the dataset. We have chosen SUBDUE as a reference method, since it is publicly available and the sub-graphs discovered by SUBDUE could be used as (propositional) features for standard machine learning algorithms. We have used the im ...
Classification and Prediction
... A testing set is independent of a training set ¨ If a training set is used for testing during learning process, the resulting model will overfit the training data à the model could incorporate anomalies in the training data that are not present in the overall sample population à model that may n ...
... A testing set is independent of a training set ¨ If a training set is used for testing during learning process, the resulting model will overfit the training data à the model could incorporate anomalies in the training data that are not present in the overall sample population à model that may n ...
Intelligent Rule Mining Algorithm for Classification over Imbalanced
... zero because the samples are treated as noise by the learning algorithm. Some classification algorithms fail to deal with imbalanced datasets completely [18][19] and classify all test samples as belonging to majority class irrespective of the feature vector. To overcome this problem, some algorithms ...
... zero because the samples are treated as noise by the learning algorithm. Some classification algorithms fail to deal with imbalanced datasets completely [18][19] and classify all test samples as belonging to majority class irrespective of the feature vector. To overcome this problem, some algorithms ...
Image Clustering For Feature Detection
... The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually invol ...
... The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually invol ...
Mouse in a Maze - Bowdoin College
... 2. What variables are needed? 3. What computations are required to achieve the output? 4. Usually, the first steps in your algorithm bring input values to the variables. 5. Usually, the last steps display the output 6. So, the middle steps will do the computation. 7. If the process is to be repeated ...
... 2. What variables are needed? 3. What computations are required to achieve the output? 4. Usually, the first steps in your algorithm bring input values to the variables. 5. Usually, the last steps display the output 6. So, the middle steps will do the computation. 7. If the process is to be repeated ...
Slides
... The year he/she published his/her first paper The number of papers of an expert The number of papers in recent 2 years The number of papers in recent 5 years The number of citations of all his/her papers The number of papers cited more than 5 times The number of papers cited more than 10 times ...
... The year he/she published his/her first paper The number of papers of an expert The number of papers in recent 2 years The number of papers in recent 5 years The number of citations of all his/her papers The number of papers cited more than 5 times The number of papers cited more than 10 times ...
Spatial Sequential Pattern Mining for Seismic Data
... detection of faults and horizons, i.e., zones of major unconformities that corresponds to specific geologic boundaries [Zhou, 2014]. Inline 100 ...
... detection of faults and horizons, i.e., zones of major unconformities that corresponds to specific geologic boundaries [Zhou, 2014]. Inline 100 ...
pdf
... Different algorithms can be derived from our paradigm by specifying how the classifier is being learned in the first phase. One option is to apply the ERM rule using a different class, that we call the approximation class. Another option is to apply a non-parametric learning method, such as Nearest ...
... Different algorithms can be derived from our paradigm by specifying how the classifier is being learned in the first phase. One option is to apply the ERM rule using a different class, that we call the approximation class. Another option is to apply a non-parametric learning method, such as Nearest ...
Smart Phone Based Data Mining for Human Activity Recognition
... 128, 256 and 561(all features), ranked by information gain approach. The total data size for training and testing comprised around 10,000 samples. We used 5 fold cross validation for partitioning this large dataset (10,000 samples) into training and testing subsets. Figure 2 shows the comparative pe ...
... 128, 256 and 561(all features), ranked by information gain approach. The total data size for training and testing comprised around 10,000 samples. We used 5 fold cross validation for partitioning this large dataset (10,000 samples) into training and testing subsets. Figure 2 shows the comparative pe ...
Distributed Privacy-Preserving Data Mining with Geometric Data
... PI=1- 2-I(X; X^), X^ is the estimation of X I(X; X^) = H(X) – H(X|X^) = H(X) – H(estimation error) Exact estimation H(X|X^) =0, PI = 1-2-H(X) Random estimation I(X; X^) = 0, PI=0 ...
... PI=1- 2-I(X; X^), X^ is the estimation of X I(X; X^) = H(X) – H(X|X^) = H(X) – H(estimation error) Exact estimation H(X|X^) =0, PI = 1-2-H(X) Random estimation I(X; X^) = 0, PI=0 ...
Document
... KEEL allows us to perform a complete analysis of any learning model in comparison to existing ones, including a statistical test module for comparison. ...
... KEEL allows us to perform a complete analysis of any learning model in comparison to existing ones, including a statistical test module for comparison. ...
association rule discovery for student performance
... and other assignments. This correlated information will be conveyed to the teacher before the transfer of final exam. This study helps the teachers to improve the performance of students and reduce the failing ratio by taking appropriate steps at on time [3]. Baradwaj and Pal, in the year 2011, used ...
... and other assignments. This correlated information will be conveyed to the teacher before the transfer of final exam. This study helps the teachers to improve the performance of students and reduce the failing ratio by taking appropriate steps at on time [3]. Baradwaj and Pal, in the year 2011, used ...
Predicting the Risk of Heart Attacks using Neural Network and
... information with useful knowledge. The discovered knowledge will be used by the healthcare administrators to predict some of the diseases and problems like heart attacks. Predicting patient’s behaviour in the future is the main application of data mining techniques. A formal definition of knowledge ...
... information with useful knowledge. The discovered knowledge will be used by the healthcare administrators to predict some of the diseases and problems like heart attacks. Predicting patient’s behaviour in the future is the main application of data mining techniques. A formal definition of knowledge ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.