Data Mining and Machine Learning Techniques
... objectives, the prediction of biological activities from chemical structures, are closely related, there are fundamental differences between both application areas. SAR studies in pharmaceutical research rely typically on a few (several tens to hundreds) compounds, containing a basic structure respo ...
... objectives, the prediction of biological activities from chemical structures, are closely related, there are fundamental differences between both application areas. SAR studies in pharmaceutical research rely typically on a few (several tens to hundreds) compounds, containing a basic structure respo ...
Clustering Methods
... C-R Lin and M-S Chen, “ Combining partitional and hierarchical algorithms for robust and efficient data clustering with cohesion self-merging”, TKDE, 17(2), 2005. ...
... C-R Lin and M-S Chen, “ Combining partitional and hierarchical algorithms for robust and efficient data clustering with cohesion self-merging”, TKDE, 17(2), 2005. ...
Detecting Outliers in Data streams using Clustering Algorithms
... achieves by representing point per cluster its allow CURE to adjust well to the geometry of non-spherical shapes and the reduction helps to reduce the effects of outliers. The combination of random sampling and partitioning and the experimental results confirm that the quality of clusters produced b ...
... achieves by representing point per cluster its allow CURE to adjust well to the geometry of non-spherical shapes and the reduction helps to reduce the effects of outliers. The combination of random sampling and partitioning and the experimental results confirm that the quality of clusters produced b ...
SAWTOOTH: Learning on huge amounts of data
... achieve the best possible classification accuracy at any given point in time. Even though many algorithms have been offered to handle concept drift, the scalability problem still remains open for such algorithms. In this thesis we propose a scalable algorithm for data classification from very large ...
... achieve the best possible classification accuracy at any given point in time. Even though many algorithms have been offered to handle concept drift, the scalability problem still remains open for such algorithms. In this thesis we propose a scalable algorithm for data classification from very large ...
Processing and classification of protein mass spectra - (CUI)
... of automatic peptide/protein identification for MS/MS data as well. Gentzel et al. (2003) investigated the influence of peak clustering, contaminant exclusion, deisotoping, clustering of similar spectra, and external calibration on protein identification. The first step was necessary since the high ...
... of automatic peptide/protein identification for MS/MS data as well. Gentzel et al. (2003) investigated the influence of peak clustering, contaminant exclusion, deisotoping, clustering of similar spectra, and external calibration on protein identification. The first step was necessary since the high ...
Working with Data in WEKA
... The weka.filters package is concerned with classes that transforms datasets -- by removing or adding attributes, resampling the dataset, removing examples and so on. This package offers useful support for data preprocessing, which is an important step in machine learning. All filters offer the optio ...
... The weka.filters package is concerned with classes that transforms datasets -- by removing or adding attributes, resampling the dataset, removing examples and so on. This package offers useful support for data preprocessing, which is an important step in machine learning. All filters offer the optio ...
Using Categorical Attributes for Clustering
... clustering approach based on the work by San et al [14] and using the distance metric used in Dutta and Mohanta’s work for categorical data clustering [15]. San et al proposed an extension to the k-means algorithm[7] for clustering categorical data taking cluster representatives as sets with the ele ...
... clustering approach based on the work by San et al [14] and using the distance metric used in Dutta and Mohanta’s work for categorical data clustering [15]. San et al proposed an extension to the k-means algorithm[7] for clustering categorical data taking cluster representatives as sets with the ele ...
Preprocessing input data for machine learning by FCA - CEUR
... Decision trees represent the most commonly used method in data mining and machine learning [13, 14]. A decision tree can be considered as a tree representation of a function over attributes which takes a finite number of values called class labels. The function is partially defined by a set of vecto ...
... Decision trees represent the most commonly used method in data mining and machine learning [13, 14]. A decision tree can be considered as a tree representation of a function over attributes which takes a finite number of values called class labels. The function is partially defined by a set of vecto ...
Cluster Analysis: Advanced Concepts d Al i h and Algorithms Outline
... Shared Nearest Neighbor (SNN) graph: the weight of an edge is the number of shared neighbors between vertices given that the ...
... Shared Nearest Neighbor (SNN) graph: the weight of an edge is the number of shared neighbors between vertices given that the ...
Towards Systematic Design of Distance Functions for Data Mining
... the best possible distance function may vary considerably even across different image domains. It is often difficult to predict the best distance function in such cases a-priori without some kind of human intervention. • The similarity between two different digital music data sets may be defined by ...
... the best possible distance function may vary considerably even across different image domains. It is often difficult to predict the best distance function in such cases a-priori without some kind of human intervention. • The similarity between two different digital music data sets may be defined by ...
Cluster Analysis 1 - Computer Science, Stony Brook University
... common similarity measures. The property of a dataset determines which one to use. 4. K-medoids clustering (PAM) uses actual objects to represent clusters. It is more robust to outliers than K-means, but the computation is costly. 5. Hierarchical clustering uses similarity matrix to cluster datase ...
... common similarity measures. The property of a dataset determines which one to use. 4. K-medoids clustering (PAM) uses actual objects to represent clusters. It is more robust to outliers than K-means, but the computation is costly. 5. Hierarchical clustering uses similarity matrix to cluster datase ...
A Two-Step Method for Clustering Mixed Categroical and Numeric
... applies the decrease in log-likelihood function as a result of merging for distance measure. This method improves k-prototype by solving the binary distance problem. Additionally, this algorithm constructs CF-tree [5] to find dense regions to form subsets, and applies hierarchical clustering algorit ...
... applies the decrease in log-likelihood function as a result of merging for distance measure. This method improves k-prototype by solving the binary distance problem. Additionally, this algorithm constructs CF-tree [5] to find dense regions to form subsets, and applies hierarchical clustering algorit ...
A Suitability Study of Discretization Methods for Associative Classifiers
... The goodness can vary depending on the penalty per error. This method needs no user input parameters. The method calculates the initial intervals with a simple discretization method and then maximizes the purity of intervals. This also makes the number of intervals very high. Then it combines the in ...
... The goodness can vary depending on the penalty per error. This method needs no user input parameters. The method calculates the initial intervals with a simple discretization method and then maximizes the purity of intervals. This also makes the number of intervals very high. Then it combines the in ...
Improved Decision Tree Methodology for the Attributes of Unknown
... To select the most informative test, the information gain for all the available test attributes is computed and the test with the maximum information gain is then selected. Although the information gain test selection criterion has been experimentally shown to lead to good decision trees in many cas ...
... To select the most informative test, the information gain for all the available test attributes is computed and the test with the maximum information gain is then selected. Although the information gain test selection criterion has been experimentally shown to lead to good decision trees in many cas ...
Levelwise Search and Borders of Theories in Knowledge Discovery
... related problems.) A specialization relation is a partial order ¹ on the sentences in L. We say that ϕ is more general than θ, if ϕ ¹ θ ; we also say that θ is more specific than ϕ. The relation ¹ is a monotone specialization relation with respect to q if the selection predicate q is monotone with r ...
... related problems.) A specialization relation is a partial order ¹ on the sentences in L. We say that ϕ is more general than θ, if ϕ ¹ θ ; we also say that θ is more specific than ϕ. The relation ¹ is a monotone specialization relation with respect to q if the selection predicate q is monotone with r ...
A Survey On Clustering Techniques For Mining Big Data
... a million of bytes of data related to their consumers, dealers and their related operation, and trillions of n/w sensors are used to establish (set) in the real world in storage spaces or devices like automobiles and mobile phone, for creating, and sensing and communicating data it needs smart phone ...
... a million of bytes of data related to their consumers, dealers and their related operation, and trillions of n/w sensors are used to establish (set) in the real world in storage spaces or devices like automobiles and mobile phone, for creating, and sensing and communicating data it needs smart phone ...
the Stream Mill Experience
... real, and one timestamp. The resulting vertical tuples always have three attributes, first attribute is an integer, namely column number, which is self-explanatory. Second attribute is a real and acts like an entry in Weka real array. The third and final attribute is the number of columns in the dat ...
... real, and one timestamp. The resulting vertical tuples always have three attributes, first attribute is an integer, namely column number, which is self-explanatory. Second attribute is a real and acts like an entry in Weka real array. The third and final attribute is the number of columns in the dat ...
www.cs.gmu.edu - George Mason University Department of
... Andre Fabiano de Moraes, Lia Bastos , Framework of Integration for Collaboration and Spatial Data Mining Among Heterogeneous Sources in the Web . . . . . . . . . . ...
... Andre Fabiano de Moraes, Lia Bastos , Framework of Integration for Collaboration and Spatial Data Mining Among Heterogeneous Sources in the Web . . . . . . . . . . ...
From Sound to “Sense” via Feature Extraction and Machine Learning
... Inductive learning as the automatic construction of classifiers from pre-classified training examples has a long tradition in several sub-fields of computer science. The field of statistical pattern classification Duda et al. [2001], Hastie et al. [2001] has developed a multitude of methods for deri ...
... Inductive learning as the automatic construction of classifiers from pre-classified training examples has a long tradition in several sub-fields of computer science. The field of statistical pattern classification Duda et al. [2001], Hastie et al. [2001] has developed a multitude of methods for deri ...
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.