1. Statistics, Primary and Secondary data, Classification and
... 1. Function, Limit and Permutation and Combinations: Concept of function of a single variable (Linear, Quadratic and exponential function only) Domain, Co-domain and Range of a Function. Types of a function. Simple example of a function.Concept of Limit, Rules of limit (Without proof) Simple example ...
... 1. Function, Limit and Permutation and Combinations: Concept of function of a single variable (Linear, Quadratic and exponential function only) Domain, Co-domain and Range of a Function. Types of a function. Simple example of a function.Concept of Limit, Rules of limit (Without proof) Simple example ...
Clustering and Prediction: some thoughts Goal of this talk
... it cannot tell you which algorithm is better it can only answer specific and partial questions once the definitions have been ...
... it cannot tell you which algorithm is better it can only answer specific and partial questions once the definitions have been ...
Information extraction and knowledge discovery from high
... the requisite intelligence to recognize them as such, they are simply bypassed and never seen by planetary scientists.” The MER rovers, for example, did not have on-board processing of science data with sufficient intelligent understanding to recognize a rare mineralogy or other scientifically rele ...
... the requisite intelligence to recognize them as such, they are simply bypassed and never seen by planetary scientists.” The MER rovers, for example, did not have on-board processing of science data with sufficient intelligent understanding to recognize a rare mineralogy or other scientifically rele ...
Improving Activity Discovery with Automatic Neighborhood
... Perceptual primitives can be thought of as recurring patterns or motifs of the data; that is, they are sets of subsequences with high similarity. The goal of this work is to discover the motifs and the corresponding occurrences in realvalued, multivariate data. The problem is complicated because lit ...
... Perceptual primitives can be thought of as recurring patterns or motifs of the data; that is, they are sets of subsequences with high similarity. The goal of this work is to discover the motifs and the corresponding occurrences in realvalued, multivariate data. The problem is complicated because lit ...
Figure 5: Fisher iris data set vote matrix after ordering.
... take the dataset properties that need to be clustered and start out by classifying the dataset in such a way that each sample represents a cluster. Next, it merges the clusters in steps. Each step merges two clusters into a single cluster until only one cluster (the dataset) remains. The algorithms ...
... take the dataset properties that need to be clustered and start out by classifying the dataset in such a way that each sample represents a cluster. Next, it merges the clusters in steps. Each step merges two clusters into a single cluster until only one cluster (the dataset) remains. The algorithms ...
An Efficient Approach to Clustering in Large Multimedia
... are applicable to databases of high-dimensional feature vectors are the methods which are known from the area of spatial data mining. The most prominent representatives are partitioning algorithms such as CLARANS [NH94], hierarchical clustering algorithms,and locality-based clustering algorithms suc ...
... are applicable to databases of high-dimensional feature vectors are the methods which are known from the area of spatial data mining. The most prominent representatives are partitioning algorithms such as CLARANS [NH94], hierarchical clustering algorithms,and locality-based clustering algorithms suc ...
Improving accuracy of students` final grade prediction model using
... methods to deal with imbalanced datasets. Chawla et al. (2002) found that majority class and minority class both have to equally represent in classification category for balanced dataset. They used combination of the method of over sampling the minority class and under sampling the majority class to ...
... methods to deal with imbalanced datasets. Chawla et al. (2002) found that majority class and minority class both have to equally represent in classification category for balanced dataset. They used combination of the method of over sampling the minority class and under sampling the majority class to ...
An Analytical Study of the Remote Sensing Image Classification
... Optimization, Ant Colony Optimization, Cuckoo Search, Particle Swarm Optimization, Artificial Bee Colony Optimization, Firefly Algorithm etc. Swarm Intelligence is a global research area to improve the optimization of various soft computing and nature inspired techniques. In this paper, we are using ...
... Optimization, Ant Colony Optimization, Cuckoo Search, Particle Swarm Optimization, Artificial Bee Colony Optimization, Firefly Algorithm etc. Swarm Intelligence is a global research area to improve the optimization of various soft computing and nature inspired techniques. In this paper, we are using ...
Duplicate Detection Algorithm In Hierarchical Data Using Efficient And Effective Network
... To compare two candidates, an overlay between their two sub trees is computed. It is not possible to match the same XML elements in different contexts. The weight is assigned to a match is based on a distance measure, e.g., edit distance for string values in leaves. The goal is to determine an overl ...
... To compare two candidates, an overlay between their two sub trees is computed. It is not possible to match the same XML elements in different contexts. The weight is assigned to a match is based on a distance measure, e.g., edit distance for string values in leaves. The goal is to determine an overl ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... Abstract: Clustering in data mining is a discovery process that groups a set of data so as to maximize the intracluster similarity and to minimize the inter-cluster similarity. The K-Means algorithm is best suited for clustering large numeric data sets when at possess only numeric values. The K-Mode ...
... Abstract: Clustering in data mining is a discovery process that groups a set of data so as to maximize the intracluster similarity and to minimize the inter-cluster similarity. The K-Means algorithm is best suited for clustering large numeric data sets when at possess only numeric values. The K-Mode ...
ITCS 6265/8265 Project
... Naive Bayes Classification The naive Bayesian classifier, is based on Bayes theorem: 1. Each data sample is represented by an n-dimensional feature vector, X = (x1, x2, ..., xn), depicting n measurements made on the sample from n attributes, respectively A1, A2, ..., An. For our problem we have 86 ...
... Naive Bayes Classification The naive Bayesian classifier, is based on Bayes theorem: 1. Each data sample is represented by an n-dimensional feature vector, X = (x1, x2, ..., xn), depicting n measurements made on the sample from n attributes, respectively A1, A2, ..., An. For our problem we have 86 ...
A Comparative Analysis of Various Clustering Techniques
... into same clusters are closer to center mean values so that the sum of squared distance from mean within each clusters is minimum.There are two types of partitioning algorithm. 1) Center based k-mean algorithm 2) Medoid based k-mode algorithm. The k-means method partitions the data objects into k cl ...
... into same clusters are closer to center mean values so that the sum of squared distance from mean within each clusters is minimum.There are two types of partitioning algorithm. 1) Center based k-mean algorithm 2) Medoid based k-mode algorithm. The k-means method partitions the data objects into k cl ...
Visual-Interactive Segmentation of Multivariate Time Series
... our scenario, we choose a rotating-arms motion. The domain expert provided a labeling of the data dividing the circular motion into six segments (‘UpToPeak’, ‘UpToMid’, ‘MidToDown’, ‘DownToMid’, ‘MidToUp’, and ‘UpToPeak’). Every human pose in the data (a time frame) is characterized by series of 3D ...
... our scenario, we choose a rotating-arms motion. The domain expert provided a labeling of the data dividing the circular motion into six segments (‘UpToPeak’, ‘UpToMid’, ‘MidToDown’, ‘DownToMid’, ‘MidToUp’, and ‘UpToPeak’). Every human pose in the data (a time frame) is characterized by series of 3D ...
What is Classification/Prediction?
... A probabilistic framework for solving classification problems Probabilistic learning: Calculate explicit probabilities for hypothesis, among the most practical approaches to certain types of learning problems Incremental: Each training example can incrementally increase/decrease the probability that ...
... A probabilistic framework for solving classification problems Probabilistic learning: Calculate explicit probabilities for hypothesis, among the most practical approaches to certain types of learning problems Incremental: Each training example can incrementally increase/decrease the probability that ...
Fast Hierarchical Clustering Based on Compressed Data and
... of a database D of n objects into a set of k clusters. Typical examples are the k-means [9] and the k-medoids [8] algorithms. Most hierarchical clustering algorithms such as the single link method [10] and OPTICS [1] do not construct a clustering of the database explicitly. Instead, these methods co ...
... of a database D of n objects into a set of k clusters. Typical examples are the k-means [9] and the k-medoids [8] algorithms. Most hierarchical clustering algorithms such as the single link method [10] and OPTICS [1] do not construct a clustering of the database explicitly. Instead, these methods co ...
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.