Community discovery using nonnegative matrix factorization
... hierarchical agglomerative clustering (HAC) method (Newman 2004a), the modularity optimization method (Newman 2004b) and the probabilistic method based on latent models (Zhang et al. 2007). More recently, the graph based partitioning methods (von Luxburg 2007; Weiss 1999) have aroused considerable i ...
... hierarchical agglomerative clustering (HAC) method (Newman 2004a), the modularity optimization method (Newman 2004b) and the probabilistic method based on latent models (Zhang et al. 2007). More recently, the graph based partitioning methods (von Luxburg 2007; Weiss 1999) have aroused considerable i ...
analyse input data
... • This data view is part of the rank-by-feature framework • Data belonging to one column (variable) is displayed as a histogram + box plot – Histogram shows the scale and skewness – Box plot shows the data distribution, center and spread ...
... • This data view is part of the rank-by-feature framework • Data belonging to one column (variable) is displayed as a histogram + box plot – Histogram shows the scale and skewness – Box plot shows the data distribution, center and spread ...
Full Text - MECS Publisher
... proposed method has indeed detected the two natural groups present in the given data set. Note that the proposed method is also successful in removing noise from the given data. Fig. 3(a) shows a data distribution of size 800 where data points are generated from two clusters. Both the clusters are h ...
... proposed method has indeed detected the two natural groups present in the given data set. Note that the proposed method is also successful in removing noise from the given data. Fig. 3(a) shows a data distribution of size 800 where data points are generated from two clusters. Both the clusters are h ...
Application of SAS Software in the Establishment of a Data Mart for Quality Analysis System in the Metallurgical Industry
... is much more efficient. This is because it has already pre-processed all dimensions such as statistics, classification, and lining done in advance. Therefore, it is very quick to make reports based on the data mart of the star schema model. When used for the large-scale data warehouse, the star sche ...
... is much more efficient. This is because it has already pre-processed all dimensions such as statistics, classification, and lining done in advance. Therefore, it is very quick to make reports based on the data mart of the star schema model. When used for the large-scale data warehouse, the star sche ...
Data Mining and Machine Learning Techniques
... In the present paper, the use of various data mining techniques2 for both steps is systematically explored on a database of mutagenic activities. The goal is to obtain insight into the utility of such data mining techniques for building SARs from toxicological databases. Among the data mining techni ...
... In the present paper, the use of various data mining techniques2 for both steps is systematically explored on a database of mutagenic activities. The goal is to obtain insight into the utility of such data mining techniques for building SARs from toxicological databases. Among the data mining techni ...
Explaining data patterns using background knowledge from Linked
... Knowledge Discovery in Databases (KDD) can be defined as the process of detecting hidden patterns and regularities in large amounts of data [?]. To be interpreted and understood, these patterns require the use of some background knowledge, which is not always straightforward to find. In most real wo ...
... Knowledge Discovery in Databases (KDD) can be defined as the process of detecting hidden patterns and regularities in large amounts of data [?]. To be interpreted and understood, these patterns require the use of some background knowledge, which is not always straightforward to find. In most real wo ...
Lecture 17 - The University of Texas at Dallas
... Introduce a measure based on how closely the original values of modified attribute can be estimated Challenge is to develop appropriate models Develop training set based on perturbed data Evolved from inference problem in statistical databases ...
... Introduce a measure based on how closely the original values of modified attribute can be estimated Challenge is to develop appropriate models Develop training set based on perturbed data Evolved from inference problem in statistical databases ...
DEVQ400-01 Developing OLAP Business Solutions with Analysis
... • It is not uncommon to represent multiple hierarchies in a dimension table. Ideally, the attribute names and values should be unique across the multiple hierarchies. ...
... • It is not uncommon to represent multiple hierarchies in a dimension table. Ideally, the attribute names and values should be unique across the multiple hierarchies. ...
Information Management course - Università degli Studi di Milano
... can use SQL queries for accessing databases comparable classification accuracy with other methods RainForest (VLDB’98 — Gehrke, Ramakrishnan & Ganti) Builds an AVC-list (attribute, value, class label) ...
... can use SQL queries for accessing databases comparable classification accuracy with other methods RainForest (VLDB’98 — Gehrke, Ramakrishnan & Ganti) Builds an AVC-list (attribute, value, class label) ...
A Competent Technique on Cluster Based Master Slave
... Candidate division technique assigns the candidate item sets generated from different parts of database to different processors and each processor is assigned disjoint candidates, independent of further processors.[8] The Master node prunes the transactions by removing 1infrequent itemsets and store ...
... Candidate division technique assigns the candidate item sets generated from different parts of database to different processors and each processor is assigned disjoint candidates, independent of further processors.[8] The Master node prunes the transactions by removing 1infrequent itemsets and store ...
A DATA MINING APPROACH FOR PRECISE DIAGNOSIS OF
... more efficient than standard QP solvers. The Sequential Minimal Optimization is commonly called as Platt’s SMO algorithm and it is well organized with good computational efficiency. SMO uses heuristics to partition the training problem into smaller problems that can be solved analytically. Whether o ...
... more efficient than standard QP solvers. The Sequential Minimal Optimization is commonly called as Platt’s SMO algorithm and it is well organized with good computational efficiency. SMO uses heuristics to partition the training problem into smaller problems that can be solved analytically. Whether o ...
An Efficient Clustering Based Irrelevant and Redundant Feature
... features, and irrelevant features offer no valuable information in any context. A feature selection algorithm may be expected from efficiency as well as effectiveness points of view. In the proposed work, a FAST algorithm is proposed based on these principles. FAST algorithm has various steps. In th ...
... features, and irrelevant features offer no valuable information in any context. A feature selection algorithm may be expected from efficiency as well as effectiveness points of view. In the proposed work, a FAST algorithm is proposed based on these principles. FAST algorithm has various steps. In th ...
Inter-Transaction Association Rules Mining for Rare Events Prediction
... is to find all sets of items that occur frequently in the same transaction and from those sets to derive rules that one subset of an itemset implies another. The notion of transaction is very general and includes items bought by the same customer, requests from same user, events happened on the same ...
... is to find all sets of items that occur frequently in the same transaction and from those sets to derive rules that one subset of an itemset implies another. The notion of transaction is very general and includes items bought by the same customer, requests from same user, events happened on the same ...
Data analysis and navigation in highdimensional chemical and
... charts unexplored chemical space. The compounds encountered along the way may provide valuable starting points for virtual screening. The use of neural networks based on self-organizing maps is proposed in (Matero et al. 2006). By using a tree-structured self-organizing map it is possible to constru ...
... charts unexplored chemical space. The compounds encountered along the way may provide valuable starting points for virtual screening. The use of neural networks based on self-organizing maps is proposed in (Matero et al. 2006). By using a tree-structured self-organizing map it is possible to constru ...
Data Mining in a Geospatial Decision Support system
... mining techniques shown in Figure 3. (See Harms 2001 for the detailed system.) Our overall goal is to find relationships with droughts and other climatic episodes and with agricultural outcomes, such as crop yield. The proposed techniques are intended as exploratory methods. Thus, iterative and inte ...
... mining techniques shown in Figure 3. (See Harms 2001 for the detailed system.) Our overall goal is to find relationships with droughts and other climatic episodes and with agricultural outcomes, such as crop yield. The proposed techniques are intended as exploratory methods. Thus, iterative and inte ...
HC3612711275
... of models are commonly used for naive Bayes classification. Both models essentially compute the posterior probability of a class, based on the distribution of the words in the document. These models ignore the actual position of the words in the document, and work with the “bag of words” assumption. ...
... of models are commonly used for naive Bayes classification. Both models essentially compute the posterior probability of a class, based on the distribution of the words in the document. These models ignore the actual position of the words in the document, and work with the “bag of words” assumption. ...
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.