Data Mining: Concepts and Techniques
... The boundary that minimizes the entropy function over all possible boundaries is selected as a binary discretization. The process is recursively applied to partitions obtained until some stopping criterion is met, e.g., ...
... The boundary that minimizes the entropy function over all possible boundaries is selected as a binary discretization. The process is recursively applied to partitions obtained until some stopping criterion is met, e.g., ...
Communication-Efficient Privacy-Preserving Clustering
... Bunn and Ostrovsky [7]. Oliviera and Zaı̈ane’s work [41] uses data transformation in conjunction with partition-based and hierarchical clustering algorithms, while the others use cryptographic techniques to give privacy-preserving versions of the k-means clustering algorithm. Vaidya and Clifton’s re ...
... Bunn and Ostrovsky [7]. Oliviera and Zaı̈ane’s work [41] uses data transformation in conjunction with partition-based and hierarchical clustering algorithms, while the others use cryptographic techniques to give privacy-preserving versions of the k-means clustering algorithm. Vaidya and Clifton’s re ...
data quality
... Support information processing by providing a solid platform of consolidated, historical data for analysis. “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. ...
... Support information processing by providing a solid platform of consolidated, historical data for analysis. “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. ...
Relational Methodology for Data Mining and Knowledge Discovery
... To represent the empirical content of data in accordance with the measurement theory we need to transform the data into many-sorted empirical systems. These transformations are described in [23] for such data types as pair comparisons, binary matrices, matrices of orderings, matrices of proximity an ...
... To represent the empirical content of data in accordance with the measurement theory we need to transform the data into many-sorted empirical systems. These transformations are described in [23] for such data types as pair comparisons, binary matrices, matrices of orderings, matrices of proximity an ...
data mining and crm in telecommunications
... strategies to reach those most likely to use each and every call customers make is stored their services, to increase customer loyalty in the database. These are known as call and improve customer profitability. detail records. Call detail records usually • High churn rates. Churn refers to the incl ...
... strategies to reach those most likely to use each and every call customers make is stored their services, to increase customer loyalty in the database. These are known as call and improve customer profitability. detail records. Call detail records usually • High churn rates. Churn refers to the incl ...
Veri Madenciliği - Giriş
... Statistical regression Artificial neural networks Genetic algorithms Nearest neighbour algorithms ...
... Statistical regression Artificial neural networks Genetic algorithms Nearest neighbour algorithms ...
Applying Data Mining Techniques to Discover Patterns in Context
... his mental state and possibilities of devices that he or she uses. All of these factors are called context information. According to Dey [13], the context is “any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relev ...
... his mental state and possibilities of devices that he or she uses. All of these factors are called context information. According to Dey [13], the context is “any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relev ...
PPT
... Network of databases Facilitates remote storage, integration, and retrieval of data Databases browsed by web based front-ends Can be extended to cater to ...
... Network of databases Facilitates remote storage, integration, and retrieval of data Databases browsed by web based front-ends Can be extended to cater to ...
Arabic Text Categorization Using Classification Rule Mining
... the Chi-square method has outperformed Naïve Bayes and the KNN classifiers in term of F-measure. [1] have evaluated the performance of tow popular classification algorithms C5.0 decision tree [17] and SVM on classifying Arabic text using the seven different Arabic corpora such as (Saudi News Papers, ...
... the Chi-square method has outperformed Naïve Bayes and the KNN classifiers in term of F-measure. [1] have evaluated the performance of tow popular classification algorithms C5.0 decision tree [17] and SVM on classifying Arabic text using the seven different Arabic corpora such as (Saudi News Papers, ...
Specializing CRISP-DM for Evidence Mining.
... in converting the initial raw data to the final data set, which is input to event modeling tool(s). Data preparation tasks are likely to be performed multiple times and not in any prescribed order. The tasks include table, record and attribute selection, entity recognition and co-reference resolutio ...
... in converting the initial raw data to the final data set, which is input to event modeling tool(s). Data preparation tasks are likely to be performed multiple times and not in any prescribed order. The tasks include table, record and attribute selection, entity recognition and co-reference resolutio ...
Data Mining in Practice
... • A quantitative variable of interest is given and we ask how much this variable changes when one of the relevant independent variables is changed ð Bayesian Local regression Spatial Data Mining, Michael May, Fraunhofer AIS ...
... • A quantitative variable of interest is given and we ask how much this variable changes when one of the relevant independent variables is changed ð Bayesian Local regression Spatial Data Mining, Michael May, Fraunhofer AIS ...
Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes
... Of the published methods for text classification, models that make the naive Bayes assumption of the features being independent have experimentally performed well compared to more sophisticated and computationally more expensive methods [15] (see [14] for an overview of alternate methods). Naive Bay ...
... Of the published methods for text classification, models that make the naive Bayes assumption of the features being independent have experimentally performed well compared to more sophisticated and computationally more expensive methods [15] (see [14] for an overview of alternate methods). Naive Bay ...
Visual Analytics : Definition, Process, and
... Many people are confused by the new term visual analytics and do not see a difference between the two areas. While there is certainly some overlay and some of the information visualization work is certainly highly related to visual analytics, traditional visualization work does not necessarily deal ...
... Many people are confused by the new term visual analytics and do not see a difference between the two areas. While there is certainly some overlay and some of the information visualization work is certainly highly related to visual analytics, traditional visualization work does not necessarily deal ...
Data Mining in Electronic Commerce
... Hence, the process will boost the performance of the entire data mining process and the accuracy of the data will also be high and the time needed for the actual mining will be minimise reasonably. Usually this happens if company already have an existing target data warehouse, but if not then the pr ...
... Hence, the process will boost the performance of the entire data mining process and the accuracy of the data will also be high and the time needed for the actual mining will be minimise reasonably. Usually this happens if company already have an existing target data warehouse, but if not then the pr ...
ag a de Pu sh ka rZ - 123SeminarsOnly.com
... days is massive parallel analysis of big unstructure files, whether huge web logs, FINANCIAL data, or sensor information. In some cases this is the same data being shared by human users in a content management application. However, the data performance requirements for these two uses are diametricall ...
... days is massive parallel analysis of big unstructure files, whether huge web logs, FINANCIAL data, or sensor information. In some cases this is the same data being shared by human users in a content management application. However, the data performance requirements for these two uses are diametricall ...
Paper
... 1] or [-1 1]. Hence preprocessing and normalization of data is required. The KDDCup99 format data is preprocessed. Each record in KDDCup99 format has 41 features, each of which is in one of the continuous, discrete and symbolic form, with significantly varying ranges. Based on the type of neural net ...
... 1] or [-1 1]. Hence preprocessing and normalization of data is required. The KDDCup99 format data is preprocessed. Each record in KDDCup99 format has 41 features, each of which is in one of the continuous, discrete and symbolic form, with significantly varying ranges. Based on the type of neural net ...
Web Crime Mining by Means of Data Mining
... methods are not able to obtain all influential parameters because of their high amount of human interference, therefore, using an intelligent and systematic approach for crime analysis more than ever. However, the data mining techniques can be the key solution (Keyvanpour et al., 2011). Areas of con ...
... methods are not able to obtain all influential parameters because of their high amount of human interference, therefore, using an intelligent and systematic approach for crime analysis more than ever. However, the data mining techniques can be the key solution (Keyvanpour et al., 2011). Areas of con ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.