Deep learning using genetic algorithms
... Deep Learning networks can recreate close approximations of their original objects from a compressed form. Ignoring the cost of the network itself, which is a substantial single cost, this algorithm will perform compression on any object given to it. If one assumes a perfectly trained matrix, such t ...
... Deep Learning networks can recreate close approximations of their original objects from a compressed form. Ignoring the cost of the network itself, which is a substantial single cost, this algorithm will perform compression on any object given to it. If one assumes a perfectly trained matrix, such t ...
Application of Fuzzy Classification in Bankruptcy Prediction Zijiang Yang and Guojun Gan
... identified it as the current trend an! d a direction for the future. The above sampling of the recently published literature on predicting corporate failure shows a vast number of approaches taken to the subject in an attempt to refine the classification model. Most forecasts achieve accuracy between 6 ...
... identified it as the current trend an! d a direction for the future. The above sampling of the recently published literature on predicting corporate failure shows a vast number of approaches taken to the subject in an attempt to refine the classification model. Most forecasts achieve accuracy between 6 ...
Decision Tree Generation Algorithm: ID3
... – Test data are used to estimate the accuracy of the classification rules – If the accuracy is considered acceptable, the rules can be applied to the classification of new data tuples ...
... – Test data are used to estimate the accuracy of the classification rules – If the accuracy is considered acceptable, the rules can be applied to the classification of new data tuples ...
No Slide Title
... Compute similarity as a distance between an object and each cluster center Assign a label to an object based on the minimum similarity Repeat for all objects Re-compute the cluster’s centers as a mean of all objects assign to a given cluster Repeat from Step 2 until objects do not change their label ...
... Compute similarity as a distance between an object and each cluster center Assign a label to an object based on the minimum similarity Repeat for all objects Re-compute the cluster’s centers as a mean of all objects assign to a given cluster Repeat from Step 2 until objects do not change their label ...
A Cluster-based Algorithm for Anomaly Detection in Time Series
... urban area, might experience that some equipment were presenting failure, which could lead to misunderstand data monitoring. Either, a credit card company may monitor each user transaction to look for unusual behaviors that could point to fraudulent operations. These unusual, unwanted behaviors are ...
... urban area, might experience that some equipment were presenting failure, which could lead to misunderstand data monitoring. Either, a credit card company may monitor each user transaction to look for unusual behaviors that could point to fraudulent operations. These unusual, unwanted behaviors are ...
VARIANCE ROVER SYSTEM: WEB ANALYTICS TOOL USING
... Advantage is that it is simple and has high processing speed when applied to large amount of data. K-Means calculates centroid of the clusters by taking average of the data points in the data set. Its disadvantage is that it does not yield the same result with each execution, as the resultant cluste ...
... Advantage is that it is simple and has high processing speed when applied to large amount of data. K-Means calculates centroid of the clusters by taking average of the data points in the data set. Its disadvantage is that it does not yield the same result with each execution, as the resultant cluste ...
K-Means - IFIS Uni Lübeck
... data? Need to extend the distance measurement. • Ahmad, Dey: A k-mean clustering algorithm for mixed numeric and categorical data, Data & Knowledge Engineering, Nov. 2007 ...
... data? Need to extend the distance measurement. • Ahmad, Dey: A k-mean clustering algorithm for mixed numeric and categorical data, Data & Knowledge Engineering, Nov. 2007 ...
Annexure `AAB-CD-01a` Course Title: Advance Data Mining
... Course Objectives: To demonstrate new concepts of organizing data ware house & data mining technique to drive the useful information out of the piles of data. With the growth of large amount of data today it has become necessity to explore and mine the data so that we can have hidden useful Informat ...
... Course Objectives: To demonstrate new concepts of organizing data ware house & data mining technique to drive the useful information out of the piles of data. With the growth of large amount of data today it has become necessity to explore and mine the data so that we can have hidden useful Informat ...
IFIS Uni Lübeck - Universität zu Lübeck
... One potential use of a dendrogram is to detect outliers The single isolated branch is suggestive of a data point that is very different to all others ...
... One potential use of a dendrogram is to detect outliers The single isolated branch is suggestive of a data point that is very different to all others ...
Fast Imbalanced Classification of Healthcare Data with Missing Values
... Given a training set J = {(xi , yi )}li=1 , that is a set of data points with known labels, where (xi , yi ) ∈ Rn+1 , and l and n are the numbers of data points and features, respectively, and yi ∈ {−1, 1} denotes the class label for each data point i in J . We denote by C− and C+ , the ”majority” ( ...
... Given a training set J = {(xi , yi )}li=1 , that is a set of data points with known labels, where (xi , yi ) ∈ Rn+1 , and l and n are the numbers of data points and features, respectively, and yi ∈ {−1, 1} denotes the class label for each data point i in J . We denote by C− and C+ , the ”majority” ( ...
Paper Title (use style: paper title)
... Outlier detection is important in many fields and concept about outlier factor of object is extended to the case of cluster. Both Statistical and distance based outlier detection depend on the overall or “global” distribution of the given set of data points. Data are usually not uniformly distribute ...
... Outlier detection is important in many fields and concept about outlier factor of object is extended to the case of cluster. Both Statistical and distance based outlier detection depend on the overall or “global” distribution of the given set of data points. Data are usually not uniformly distribute ...
Spatio-temporal clustering methods
... stops (i.e., the proportion is much smaller than one). Due to this, we need and online algorithm that can function without prior knowledge of stay point to path ratio. The SMoT algorithm uses predefined regions which can be a problem if we want to detect some stay points outside of those zones (such ...
... stops (i.e., the proportion is much smaller than one). Due to this, we need and online algorithm that can function without prior knowledge of stay point to path ratio. The SMoT algorithm uses predefined regions which can be a problem if we want to detect some stay points outside of those zones (such ...
17 MAKING THE DECISION ON BUYING SECOND
... achieved through analysis of existing data on the auto market using data mining techniques. The paper has determined the price of a car using linear regression and the of score each type of car, which, on a scale from 1 to 5, will show whether or not a car worths to be bought. Also there have been i ...
... achieved through analysis of existing data on the auto market using data mining techniques. The paper has determined the price of a car using linear regression and the of score each type of car, which, on a scale from 1 to 5, will show whether or not a car worths to be bought. Also there have been i ...
Classification via clustering for predicting final marks based on
... Forums are one of the most commonly used tools in web-based teaching-learning environments because they play an important role in students’ collaborative learning [12]. In fact, student activity in discussion threads can be a relevant source of information that facilitates the monitoring of tasks du ...
... Forums are one of the most commonly used tools in web-based teaching-learning environments because they play an important role in students’ collaborative learning [12]. In fact, student activity in discussion threads can be a relevant source of information that facilitates the monitoring of tasks du ...
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.