DM-6 - Computer Science Unplugged
... Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute The set of tuples used for model construction is training set The model is represented as classification rules, decision trees, ...
... Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute The set of tuples used for model construction is training set The model is represented as classification rules, decision trees, ...
Lecture notes for chapter 7
... Assume that there are two classes, P and N. Let the set of examples S contain p elements of class P and n elements of class N. The amount of information, needed to decide if an arbitrary example in S belong to P or N is defined as ...
... Assume that there are two classes, P and N. Let the set of examples S contain p elements of class P and n elements of class N. The amount of information, needed to decide if an arbitrary example in S belong to P or N is defined as ...
Yes - Computing Science - Thompson Rivers University
... The nearest neighbor algorithm works with data that consists of vectors of numerical attributes. Each vector represents a point in n-dimensional space. When an unseen data item is to be classified, the Euclidean distance is calculated between this item and all training data. For example, the distanc ...
... The nearest neighbor algorithm works with data that consists of vectors of numerical attributes. Each vector represents a point in n-dimensional space. When an unseen data item is to be classified, the Euclidean distance is calculated between this item and all training data. For example, the distanc ...
Making Subsequence Time Series Clustering Meaningful
... Definition 2 Let X be a data series and Z be the series of subsequences obtained by using the sliding windows technique on X. If we conduct a clustering on Z (ie. we are STS-clustering X) to obtain a set of clusters Cj , j = 1 . . . k, a “segment” in Cj is a set of members of Cj that were originally ...
... Definition 2 Let X be a data series and Z be the series of subsequences obtained by using the sliding windows technique on X. If we conduct a clustering on Z (ie. we are STS-clustering X) to obtain a set of clusters Cj , j = 1 . . . k, a “segment” in Cj is a set of members of Cj that were originally ...
Comparative Analysis of Various Clustering Algorithms
... to k-means and many other algorithms. Arbitrarily/ concave shaped clusters can be found in this algorithm. However, the quality of DBSCAN depends on the distance measure used. The most common distance metric used is Euclidean distance. Especially for high-dimensional data, this metric can be rendere ...
... to k-means and many other algorithms. Arbitrarily/ concave shaped clusters can be found in this algorithm. However, the quality of DBSCAN depends on the distance measure used. The most common distance metric used is Euclidean distance. Especially for high-dimensional data, this metric can be rendere ...
Machine learning in bioinformatics
... sequences of true and false donor sites along with their label. At this point, we can use this training set to build up a classifier. Once the classifier has been trained, we can use it to label new sequences, using the nucleotide present at each position as an input to the classifier and getting th ...
... sequences of true and false donor sites along with their label. At this point, we can use this training set to build up a classifier. Once the classifier has been trained, we can use it to label new sequences, using the nucleotide present at each position as an input to the classifier and getting th ...
Online Batch Weighted Ensemble for Mining Data Streams with
... of data. The M SEi can be P expressed by M SEi = |S1n | (x,c)∈Sn (1 − fci (x))2 , where Sn is the last block of data and fci (x) is the probability obtained from the classi er i that example x is an instance of class c. In each iteration, k best base classi ers are chosen to form the nal ensemble. ...
... of data. The M SEi can be P expressed by M SEi = |S1n | (x,c)∈Sn (1 − fci (x))2 , where Sn is the last block of data and fci (x) is the probability obtained from the classi er i that example x is an instance of class c. In each iteration, k best base classi ers are chosen to form the nal ensemble. ...
Scaling up classification rule induction through parallel processing
... new window and tested on the remaining instances. Windowing also applies testing first to instances that have not been tested yet and then to the already tested ones. This is repeated until all remaining instances are correctly classified. Windowing has been examined empirically in Wirth and Catlett ...
... new window and tested on the remaining instances. Windowing also applies testing first to instances that have not been tested yet and then to the already tested ones. This is repeated until all remaining instances are correctly classified. Windowing has been examined empirically in Wirth and Catlett ...
A Lattice Algorithm for Data Mining
... and randomly generated. Kuznetsov et al. [KUZ 02] compared, both theoretically and experimentally, performance of ten well-known algorithms for constructing concept lattices. The authors considered that Godin was suitable for small and sparse context, Bordat should be used for contexts of average de ...
... and randomly generated. Kuznetsov et al. [KUZ 02] compared, both theoretically and experimentally, performance of ten well-known algorithms for constructing concept lattices. The authors considered that Godin was suitable for small and sparse context, Bordat should be used for contexts of average de ...
PDF
... derived from the experimental data. This task can be posed as an induction problem, i.e. we want to extract functions having dependencies between input and output data such that the functions represent actions while the variables of the function represent attributes of objects. Various techniques ha ...
... derived from the experimental data. This task can be posed as an induction problem, i.e. we want to extract functions having dependencies between input and output data such that the functions represent actions while the variables of the function represent attributes of objects. Various techniques ha ...
OPTICS on Text Data: Experiments and Test Results
... OPTICS on text data and gathered valuable insights into the working of OPTICS and it’s applicability on text data. The SCI algorithm presented in this paper to identify clusters from the OPTICS plot can be used as a benchmark to test for the performance of OPTICS based on purity and coverage perform ...
... OPTICS on text data and gathered valuable insights into the working of OPTICS and it’s applicability on text data. The SCI algorithm presented in this paper to identify clusters from the OPTICS plot can be used as a benchmark to test for the performance of OPTICS based on purity and coverage perform ...
Gaussian Mixture Density Modeling, Decomposition, and Applications
... chemical composition, metabolism, and other measurable factors. However, as is common with most practical applications, the statistical tendency of abnormal cells cannot be easily characterized by any simple structured density. Hence, a mixture model consisting of a number of component densities can ...
... chemical composition, metabolism, and other measurable factors. However, as is common with most practical applications, the statistical tendency of abnormal cells cannot be easily characterized by any simple structured density. Hence, a mixture model consisting of a number of component densities can ...
Metro - IRD India
... Data clustering is the process which divides a dataset into some groups or classes. It lets the data objects of the same group have high similarity, and the data objects of different groups have large differences. The similarity is often using the distance between the objects. The data clustering us ...
... Data clustering is the process which divides a dataset into some groups or classes. It lets the data objects of the same group have high similarity, and the data objects of different groups have large differences. The similarity is often using the distance between the objects. The data clustering us ...
Why Functional Programming Matters --- In an Object
... lead to nearly indistinguishable programs: - data layout induces program layout - iteration patterns or iterator functions - few (true) assignments to reflect “real world” changes (history or state) - objects as “multi-bodied, multi-entry” closures ...
... lead to nearly indistinguishable programs: - data layout induces program layout - iteration patterns or iterator functions - few (true) assignments to reflect “real world” changes (history or state) - objects as “multi-bodied, multi-entry” closures ...
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.