Grouped Data
... Standard Deviation • Find a suitable empty cell. • Go to autofunctions and choose math & trig functions and click on SQRT. • Highlight the variance so that it will be used by the function. • Put the label st. dev., or s, in an empty cell above or next to your answer ...
... Standard Deviation • Find a suitable empty cell. • Go to autofunctions and choose math & trig functions and click on SQRT. • Highlight the variance so that it will be used by the function. • Put the label st. dev., or s, in an empty cell above or next to your answer ...
Selection of Initial Seed Values for K-Means Algorithm
... This paper proposes an enhancement of the performance of the traditional K-Means algorithm of Partitional clustering by using Taguchi method as an optimization technique. K-Means algorithm requires the desired number of clusters to be known in priori. Given the desired number of clusters, the initia ...
... This paper proposes an enhancement of the performance of the traditional K-Means algorithm of Partitional clustering by using Taguchi method as an optimization technique. K-Means algorithm requires the desired number of clusters to be known in priori. Given the desired number of clusters, the initia ...
data mining using integration of clustering and decision
... into different clusters such that the data within the cluster are similar to each other. Here the cluster centers are initialized with those k clusters. After initializing the cluster centers, perform partitioning by assigning or reassigning all data objects to their closest cluster center. Compute ...
... into different clusters such that the data within the cluster are similar to each other. Here the cluster centers are initialized with those k clusters. After initializing the cluster centers, perform partitioning by assigning or reassigning all data objects to their closest cluster center. Compute ...
notes #20 - Computer Science
... Measure the Quality of Clustering • Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, typically metric: d(i, j) • There is a separate “quality” function that measures the “goodness” of a cluster. • The definitions of distance functions are usually very differ ...
... Measure the Quality of Clustering • Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, typically metric: d(i, j) • There is a separate “quality” function that measures the “goodness” of a cluster. • The definitions of distance functions are usually very differ ...
Recognition of Slow Learners Using Classification Data Mining
... Machine and Random forest) on the dataset of 788 students of two schools from the Alentejo region of Portugal. After analysis, they found that Decision Tree (DT) and Neural Network (NN) had 93% and 91% accuracy in predicting the result according to two- class (pass/fail) respectively [8]. Galit.et.a ...
... Machine and Random forest) on the dataset of 788 students of two schools from the Alentejo region of Portugal. After analysis, they found that Decision Tree (DT) and Neural Network (NN) had 93% and 91% accuracy in predicting the result according to two- class (pass/fail) respectively [8]. Galit.et.a ...
Scalable Cluster Analysis of Spatial Events
... with high space utilization for each frame separately. Furthermore, in order to efficiently merge clusters from different frames, we employ a cluster label-object dictionary structure: we first identify the cluster label to be changed, and then bulk update the actual objects associated with that clu ...
... with high space utilization for each frame separately. Furthermore, in order to efficiently merge clusters from different frames, we employ a cluster label-object dictionary structure: we first identify the cluster label to be changed, and then bulk update the actual objects associated with that clu ...
A General Framework for Mining Concept-Drifting Data
... a given test example. This may work well for deterministic problems, yet is not reasonable for a stochastic application or previously deterministic problem unknowingly evolving into a stochastic one. Compared with deterministic problems, where each example strictly belongs to one class, stochastic p ...
... a given test example. This may work well for deterministic problems, yet is not reasonable for a stochastic application or previously deterministic problem unknowingly evolving into a stochastic one. Compared with deterministic problems, where each example strictly belongs to one class, stochastic p ...
A Query Optimization Application in Database Management System
... consistent patterns or to analyze the systematic relationships between data or variables and then to validate the findings by applying the detected patterns to new subsets of data. They also over viewed neural network as a collection of many processing elements called neurons and all neurons interco ...
... consistent patterns or to analyze the systematic relationships between data or variables and then to validate the findings by applying the detected patterns to new subsets of data. They also over viewed neural network as a collection of many processing elements called neurons and all neurons interco ...
Spatial clustering paper
... characterized by its geospatial attributes, normally the latitude and longitude and its physical attributes, such as size, base height, depth, and radar reflectivity. Therefore, classical clustering methods can be directly applied to storm event data. In this study, we examined two clustering algori ...
... characterized by its geospatial attributes, normally the latitude and longitude and its physical attributes, such as size, base height, depth, and radar reflectivity. Therefore, classical clustering methods can be directly applied to storm event data. In this study, we examined two clustering algori ...
cougar^2: an open source machine learning and data mining
... algorithm to check configuration parameters against the data by only inspecting feature meta-data. Unlike most other libraries, a dataset object is provided in the constructor as opposed to the training method. An algorithm may have constraints on the data or may need to do some initialization. Duri ...
... algorithm to check configuration parameters against the data by only inspecting feature meta-data. Unlike most other libraries, a dataset object is provided in the constructor as opposed to the training method. An algorithm may have constraints on the data or may need to do some initialization. Duri ...
Efficiency Improvement in Classification Tasks using Naive Bayes
... To test the proposedsystem hybrid methods, we have used the classification accuracy and 10- fold cross validation. To improve the classification accuracy rates for Naive Bayes tree (NBTREE) and Fuzzy Logic for multi class problem. ...
... To test the proposedsystem hybrid methods, we have used the classification accuracy and 10- fold cross validation. To improve the classification accuracy rates for Naive Bayes tree (NBTREE) and Fuzzy Logic for multi class problem. ...
as a PDF
... Classification is a form of supervised learning where class labels for training samples are given and used as examples to supervise the learning of a classification model. Typical classification algorithms used for data mining task are Decision Tree (DT), Artificial Neural Networks (ANN), Naïve Baye ...
... Classification is a form of supervised learning where class labels for training samples are given and used as examples to supervise the learning of a classification model. Typical classification algorithms used for data mining task are Decision Tree (DT), Artificial Neural Networks (ANN), Naïve Baye ...
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.