Data Mining and Face Recognition - International Journal of Science
... metric in input vector and all cluster Centre and determining which cluster is nearest or most similar one.So a cluster analysis has used as astandalone data-mining tool to gain insight into the data distribution, or as a preprocessing stage for different data mining algorithms running on the detect ...
... metric in input vector and all cluster Centre and determining which cluster is nearest or most similar one.So a cluster analysis has used as astandalone data-mining tool to gain insight into the data distribution, or as a preprocessing stage for different data mining algorithms running on the detect ...
Students` success prediction using Weka tool
... classification algorithm and provides possibility for decision rules creation. Because of that fact both algorithms are used for model creation and future prediction. For the future prediction which is based on these models the same classifiers need to be used. If we observe results presented in the ...
... classification algorithm and provides possibility for decision rules creation. Because of that fact both algorithms are used for model creation and future prediction. For the future prediction which is based on these models the same classifiers need to be used. If we observe results presented in the ...
A Comparative Analysis of Classification with Unlabelled Data using
... and evaluated the methods of joining two (or more) related attributes into a new compound attribute which were forward sequential selection and joining and backward sequential elimination and joining. Result shows that the domains on which the most improvement occurs are those domains on which the n ...
... and evaluated the methods of joining two (or more) related attributes into a new compound attribute which were forward sequential selection and joining and backward sequential elimination and joining. Result shows that the domains on which the most improvement occurs are those domains on which the n ...
Classification and Regression Tree Analysis
... The statistical processes behind classification and regression in tree analysis is very similar, as we will see, but it is important to clearly distinguish the two. For a response variable which has classes, often 0–1 binary,2 we want to organize the dataset into groups by the response variable – cl ...
... The statistical processes behind classification and regression in tree analysis is very similar, as we will see, but it is important to clearly distinguish the two. For a response variable which has classes, often 0–1 binary,2 we want to organize the dataset into groups by the response variable – cl ...
Improving Learning Performance Through Rational ... Jonathan Gratch*, Steve Chien+, and ...
... sum of the cost of processing each training example. Equation 3 shows that the number of examples allocated to the two hypotheses increases as the variance increases, as the difference in utility between the hypotheses decreases, or as the acceptable probability of making a mistake decreases. The fi ...
... sum of the cost of processing each training example. Equation 3 shows that the number of examples allocated to the two hypotheses increases as the variance increases, as the difference in utility between the hypotheses decreases, or as the acceptable probability of making a mistake decreases. The fi ...
Approximate Association Rule Mining
... A number of data mining algorithms have been recently developed that greatly facilitate the processing and interpreting of large stores of data. One example is the association rule mining algorithm, which discovers correlations between items in transactional databases. The Apriori algorithm is an ex ...
... A number of data mining algorithms have been recently developed that greatly facilitate the processing and interpreting of large stores of data. One example is the association rule mining algorithm, which discovers correlations between items in transactional databases. The Apriori algorithm is an ex ...
Bayesian Classification: Why?
... First decide the network topology: # of units in the input layer, # of hidden layers (if > 1), # of units in each hidden layer, and # of units in the output layer Normalizing the input values for each attribute measured in the training tuples to [0.0—1.0] One input unit per domain value, each initia ...
... First decide the network topology: # of units in the input layer, # of hidden layers (if > 1), # of units in each hidden layer, and # of units in the output layer Normalizing the input values for each attribute measured in the training tuples to [0.0—1.0] One input unit per domain value, each initia ...
Lazy Learners - Iust personal webpages
... ranges (such as income) from outweighing attributes with initially smaller ranges (such as binary attributes). Min-max normalization: ...
... ranges (such as income) from outweighing attributes with initially smaller ranges (such as binary attributes). Min-max normalization: ...
Classification Models Based-on Incremental Learning Algorithm and Feature Selection on
... soft decisions [3] [20], Mahalanobis distance measurement, and Gaussian function [3]. ILM contains two steps: learning step and predicting step that is shown in Fig. 3. First, learning step consists of two learning algorithms (cover supervised and unsupervised learning) and an incremental learning a ...
... soft decisions [3] [20], Mahalanobis distance measurement, and Gaussian function [3]. ILM contains two steps: learning step and predicting step that is shown in Fig. 3. First, learning step consists of two learning algorithms (cover supervised and unsupervised learning) and an incremental learning a ...
Change Detection in Data Streams by Testing Exchangeability
... Methodology - Strangeness Strangeness measures how well one data point (for each data point seen so far) is represented by a data model compared to other points • Applicable to classification, regression or cluster model • measure diversity / disagreements, i.e. the higher the strangeness of a poin ...
... Methodology - Strangeness Strangeness measures how well one data point (for each data point seen so far) is represented by a data model compared to other points • Applicable to classification, regression or cluster model • measure diversity / disagreements, i.e. the higher the strangeness of a poin ...
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.