MapReduce-based Backpropagation Neural Network over Large
... Backpropagation [4] (BP) is one of the most widely used algorithms for supervised learning with multi-layered feedforward neural networks [5]. The weights update in BP algorithm has two modes: the online mode and the batch mode. The batch update mode is more suitable for MapReduce framework of the c ...
... Backpropagation [4] (BP) is one of the most widely used algorithms for supervised learning with multi-layered feedforward neural networks [5]. The weights update in BP algorithm has two modes: the online mode and the batch mode. The batch update mode is more suitable for MapReduce framework of the c ...
An Efficient Multi-set HPID3 Algorithm based on RFM Model
... information from databases. Exploiting large volumes of data for superior decision making by looking for interesting patterns in the data has become a main task in today’s business environment. Data classification is one of the most widely used technologies in data mining. Its main purpose is to bui ...
... information from databases. Exploiting large volumes of data for superior decision making by looking for interesting patterns in the data has become a main task in today’s business environment. Data classification is one of the most widely used technologies in data mining. Its main purpose is to bui ...
Data Mining: Classification Techniques of Students
... help the administration using technology to make a quick decision. Data mining aims to discover useful information or knowledge by using one of data mining techniques, this paper used classification technique to discover knowledge from student’s server database, where all students’ information were ...
... help the administration using technology to make a quick decision. Data mining aims to discover useful information or knowledge by using one of data mining techniques, this paper used classification technique to discover knowledge from student’s server database, where all students’ information were ...
Learning with Local Models
... would be the vertical line through the large circle, classifying all points to its right as negative and the points to its left as positive. In contrast to the local pattern case, the smaller circle will only be a local model if its class is negative, i. e. different from the one predicted by the d ...
... would be the vertical line through the large circle, classifying all points to its right as negative and the points to its left as positive. In contrast to the local pattern case, the smaller circle will only be a local model if its class is negative, i. e. different from the one predicted by the d ...
Outlier Detection - Department of Computer Science
... Plug each point p into the density function dM of model M and compute dM(p) or preferably log(dM(p)), called the log likelihood of p, and add this value as in a new column ols (“outlier score”) to D obtaining D’—the smaller this value is the more likely p is an outlier with respect M. Sort D’ in asc ...
... Plug each point p into the density function dM of model M and compute dM(p) or preferably log(dM(p)), called the log likelihood of p, and add this value as in a new column ols (“outlier score”) to D obtaining D’—the smaller this value is the more likely p is an outlier with respect M. Sort D’ in asc ...
PDF
... information. The datasets in data mining applications are often large and so new classification techniques have been developed and are being developed to deal with millions of objects having perhaps dozens or even hundreds of attributes. Hence classifying these data sets becomes an important problem ...
... information. The datasets in data mining applications are often large and so new classification techniques have been developed and are being developed to deal with millions of objects having perhaps dozens or even hundreds of attributes. Hence classifying these data sets becomes an important problem ...
DM_04_06_Nearest-Nei.. - 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: ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... very high-dimensional data. Mining and understanding such high-dimensional data is a contemporary challenge due to the curse of dimensionality. However, the current researches [3,4,5,6,13] show that reducing the dimensionality of data has been a feasible method for more precisely charactering such d ...
... very high-dimensional data. Mining and understanding such high-dimensional data is a contemporary challenge due to the curse of dimensionality. However, the current researches [3,4,5,6,13] show that reducing the dimensionality of data has been a feasible method for more precisely charactering such d ...
Decision Support System on Prediction of Heart Disease Using Data
... Yes / Total no. of Records P (HeartDis N) = No.of Records with Result No / Total no. of Records P (t/yes) = P (Age (low) yes) * P (Sex (Male) yes) * P (BP (High) yes) * P (Chol (High) yes) * P (Heart_Rate (High)yes)*P(Vessels(High)yes)*P(Chest_Pain(High)yes)*P(ECG(High)yes)*P(Exer_angina(High)yes)*P ...
... Yes / Total no. of Records P (HeartDis N) = No.of Records with Result No / Total no. of Records P (t/yes) = P (Age (low) yes) * P (Sex (Male) yes) * P (BP (High) yes) * P (Chol (High) yes) * P (Heart_Rate (High)yes)*P(Vessels(High)yes)*P(Chest_Pain(High)yes)*P(ECG(High)yes)*P(Exer_angina(High)yes)*P ...
Preprocessing of Various Data Sets Using Different Classification
... categories to describe a data set according to similarities among its objects.[3] Clustering techniques are classified as partitioned, hierarchical and non-exclusive ie., over lapping methods. When we use the machine learning data set that is when we are in an unsupervised nature, the clustering tec ...
... categories to describe a data set according to similarities among its objects.[3] Clustering techniques are classified as partitioned, hierarchical and non-exclusive ie., over lapping methods. When we use the machine learning data set that is when we are in an unsupervised nature, the clustering tec ...
Ensemble of Clustering Algorithms for Large Datasets
... this instability of results considerably complicates the configuration of parameters of the algorithm. It is known [10–12] that the stability of solutions in clustering problems can be increased by the formation of an ensemble of algorithms and construction of a collective solution on its basis. This ...
... this instability of results considerably complicates the configuration of parameters of the algorithm. It is known [10–12] that the stability of solutions in clustering problems can be increased by the formation of an ensemble of algorithms and construction of a collective solution on its basis. This ...
CSE601 Clustering Advanced
... – High consistency between the partitioning and the domain knowledge ...
... – High consistency between the partitioning and the domain 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.