Adaptive hybrid methods for Feature selection based on
... aim of this paper is to highlight the need for feature selection methods in data mining encompassing the best characteristics of the data. In recent times there has been interest in developing hybrid feature selection methods combining the characteristics of various filter and wrapper methods. The p ...
... aim of this paper is to highlight the need for feature selection methods in data mining encompassing the best characteristics of the data. In recent times there has been interest in developing hybrid feature selection methods combining the characteristics of various filter and wrapper methods. The p ...
CVFDT algorithm
... In order to find the best attribute at a node, it may be sufficient to consider only a small subset of the training examples that pass through that node. Given a stream of examples, use the first ones to choose the root attribute. Once the root attribute is chosen, the successive examples are ...
... In order to find the best attribute at a node, it may be sufficient to consider only a small subset of the training examples that pass through that node. Given a stream of examples, use the first ones to choose the root attribute. Once the root attribute is chosen, the successive examples are ...
Big Data Analytics
... 6. Implementing any one Clustering algorithm using Map-Reduce 7. Implementing any one data streaming algorithm using Map-Reduce 8. Mini Project: One real life large data application to be implemented (Use standard Datasets available on the web) a) Twitter data analysis b) Fraud Detection c) Text Min ...
... 6. Implementing any one Clustering algorithm using Map-Reduce 7. Implementing any one data streaming algorithm using Map-Reduce 8. Mini Project: One real life large data application to be implemented (Use standard Datasets available on the web) a) Twitter data analysis b) Fraud Detection c) Text Min ...
COMP3420: dvanced Databases and Data Mining
... Documents and user queries are represented as m-dimensional vectors, where m is the total number of index terms in the document collection The degree of similarity of the document d with regard to the query q is calculated as the correlation between the vectors that represent them, using measure ...
... Documents and user queries are represented as m-dimensional vectors, where m is the total number of index terms in the document collection The degree of similarity of the document d with regard to the query q is calculated as the correlation between the vectors that represent them, using measure ...
A Study on Market Basket Analysis Using a Data Mining
... knowledge from data and various algorithms have been proposed so far. But the problem is that typically not all rules are interesting - only small fractions of the generated rules would be of interest to any given user. Hence, numerous measures such as confidence, support, lift, information gain, an ...
... knowledge from data and various algorithms have been proposed so far. But the problem is that typically not all rules are interesting - only small fractions of the generated rules would be of interest to any given user. Hence, numerous measures such as confidence, support, lift, information gain, an ...
Research on Pattern Analysis and Data Classification
... Data and pattern classification is used to classify each item in a set of cluster of data into one of predefined set of groups. Classification is a function of data mining, distributed collection in a project goal category or class. Classification is the purpose of accurately predicting each case of ...
... Data and pattern classification is used to classify each item in a set of cluster of data into one of predefined set of groups. Classification is a function of data mining, distributed collection in a project goal category or class. Classification is the purpose of accurately predicting each case of ...
Classification Of Complex UCI Datasets Using Machine Learning
... C4.5 or improved version of the C4.5. The output given by J48 is the Decision tree. A Decision tree is same as that of the tree structure. having different nodes, such as root node, intermediate nodes and leaf node. Each node in the tree contains a decision and that decision leads to our result as n ...
... C4.5 or improved version of the C4.5. The output given by J48 is the Decision tree. A Decision tree is same as that of the tree structure. having different nodes, such as root node, intermediate nodes and leaf node. Each node in the tree contains a decision and that decision leads to our result as n ...
Classification and Regression Trees as a Part of Data
... decision tree, such that each child node is made of a group of homogeneous values of the selected field. This process continues recursively until the tree is fully grown. The statistical test used depends upon the measurement level of the target field. If the target field is continuous, an F test is ...
... decision tree, such that each child node is made of a group of homogeneous values of the selected field. This process continues recursively until the tree is fully grown. The statistical test used depends upon the measurement level of the target field. If the target field is continuous, an F test is ...
Distance-based and Density-based Algorithm for Outlier Detection
... the neighborhood and local density. While these methods give assurance in improved performance compared to methods inspired on statistical or computational geometry principles, they are not scalable. The well known nearest-neighbor principle which is distance based was first proposed by Ng and Knorr ...
... the neighborhood and local density. While these methods give assurance in improved performance compared to methods inspired on statistical or computational geometry principles, they are not scalable. The well known nearest-neighbor principle which is distance based was first proposed by Ng and Knorr ...
clusters
... associated with one of the k-dimensions, such that the hyperplane is perpendicular to that dimension vector. ...
... associated with one of the k-dimensions, such that the hyperplane is perpendicular to that dimension vector. ...
EasySDM: A Spatial Data Mining Platform
... data mining algorithms, which need the geographically pre-processed spatial data and the associated Shape file. EasySDM offers four categories of clustering; the partitioning, the density, the hierarchical and finally the regionalization clustering. ...
... data mining algorithms, which need the geographically pre-processed spatial data and the associated Shape file. EasySDM offers four categories of clustering; the partitioning, the density, the hierarchical and finally the regionalization clustering. ...
Branko Kavšek, Nada Lavrač - ailab
... consisting of 3 related sets of data: the ACCIDENT data, the VEHICLE data and the CASUALTY data. The ACCIDENT data consists of the records of all accidents happened over the given period of time; VEHICLE data includes data about all the vehicles involved in those accidents; CASUALTY data includes th ...
... consisting of 3 related sets of data: the ACCIDENT data, the VEHICLE data and the CASUALTY data. The ACCIDENT data consists of the records of all accidents happened over the given period of time; VEHICLE data includes data about all the vehicles involved in those accidents; CASUALTY data includes th ...
A Data Mining Algorithm In Distance Learning
... transactions for both algorithms, where the average size of transactions varies from 4 to 14 for the synthetic dataset. As the average size of transactions increases, the runtime of the algorithm in Ref [2] increases dramatically, however, compared to the algorithm in Ref [2], the runtime of our pro ...
... transactions for both algorithms, where the average size of transactions varies from 4 to 14 for the synthetic dataset. As the average size of transactions increases, the runtime of the algorithm in Ref [2] increases dramatically, however, compared to the algorithm in Ref [2], the runtime of our pro ...
Comparative Study of Different Data Mining Prediction
... only if dimensions are properly defined. If one of the dimensional values changes, then output also will change. The multi-dimensional analysis tools are capable of handling large data sets. These tools provide facilities to define convenient hierarchies and dimensions. But they are unable to predic ...
... only if dimensions are properly defined. If one of the dimensional values changes, then output also will change. The multi-dimensional analysis tools are capable of handling large data sets. These tools provide facilities to define convenient hierarchies and dimensions. But they are unable to predic ...
Automatic Classification of Location Contexts with Decision Trees
... Commercially available geographic databases usually contain a set of geographic features about a certain region. These often include the administrative boundaries of countries, districts and municipalities, the geometric representation of rivers and roads, the position of cities, airports, railway s ...
... Commercially available geographic databases usually contain a set of geographic features about a certain region. These often include the administrative boundaries of countries, districts and municipalities, the geometric representation of rivers and roads, the position of cities, airports, railway s ...
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.