Combining Ontology Alignment Metrics Using the Data Mining
... In what following, we analysis the problem using Neural Networks as well as CART 2 and C5.0 decision tress[6]. As mentioned before, columns of the table corresponding to values of metrics are considered as Predictors and the actual mapping value is the target variable. Fig. 1 shows the process. The ...
... In what following, we analysis the problem using Neural Networks as well as CART 2 and C5.0 decision tress[6]. As mentioned before, columns of the table corresponding to values of metrics are considered as Predictors and the actual mapping value is the target variable. Fig. 1 shows the process. The ...
Review of Existing Methods for Finding Initial Clusters in K
... into subsets such that the data elements in a cluster are similar to one another and different from the elements of other clusters [1]. The set of clusters resulting from a cluster analysis can be referred to as a clustering. In this context, different clustering methods may generate different clust ...
... into subsets such that the data elements in a cluster are similar to one another and different from the elements of other clusters [1]. The set of clusters resulting from a cluster analysis can be referred to as a clustering. In this context, different clustering methods may generate different clust ...
Data mining on graphics processors
... TBI-GPU performs better than TBI CPU in data set Retail and Chess, and PBI-GPU performs better than TBI-GPU in most of the cases. PBI-GPU, TBI-GPU and TBI-CPU, implemented by this paper perform better than the original BORGELT and GOETHALS. The paper also compared the FP-GROWTH with GPU based implem ...
... TBI-GPU performs better than TBI CPU in data set Retail and Chess, and PBI-GPU performs better than TBI-GPU in most of the cases. PBI-GPU, TBI-GPU and TBI-CPU, implemented by this paper perform better than the original BORGELT and GOETHALS. The paper also compared the FP-GROWTH with GPU based implem ...
Clustering
... Automatically identifying subspaces of a high dimensional data space that allow better clustering than original space ...
... Automatically identifying subspaces of a high dimensional data space that allow better clustering than original space ...
Advances in Natural and Applied Sciences Metaheuristics for Mining
... The rise of Meta-Heuristics can mainly be attributed to the increase in data generation. This is due to the increase in the information leveraging devices such as sensors, high resolution cameras and video recorders, satellites and user information generated from the internet. Hence a huge amount of ...
... The rise of Meta-Heuristics can mainly be attributed to the increase in data generation. This is due to the increase in the information leveraging devices such as sensors, high resolution cameras and video recorders, satellites and user information generated from the internet. Hence a huge amount of ...
SPMF: A Java Open-Source Pattern Mining Library
... The source code can be easily integrated into other Java software programs since (1) the source code of each algorithm implementation is located in its own subpackage and (2) there is no dependency on any other software or library. To support developers and users, extensive resources are provided on ...
... The source code can be easily integrated into other Java software programs since (1) the source code of each algorithm implementation is located in its own subpackage and (2) there is no dependency on any other software or library. To support developers and users, extensive resources are provided on ...
Information Sharing across Private Databases
... Negative Results: cannot give high quality statistics and simultaneously prevent partial disclosure of individual information [AW89] ...
... Negative Results: cannot give high quality statistics and simultaneously prevent partial disclosure of individual information [AW89] ...
here - School of Computer Science
... monthly MAE for all stations (Fig. 2) follows the average magnitude of solar energy by month, with the smallest error in December and January, then increasing to the highest error in May and June. All of the contestants have very similar monthly errors, Fig. 3. MAE at each Mesonet site for the top f ...
... monthly MAE for all stations (Fig. 2) follows the average magnitude of solar energy by month, with the smallest error in December and January, then increasing to the highest error in May and June. All of the contestants have very similar monthly errors, Fig. 3. MAE at each Mesonet site for the top f ...
Magical Thinking in Data Mining: Lessons From CoIL Challenge 2000
... from combinations of existing attributes. Both boosting and derived attributes are ways of relaxing the conditional independence assumptions that constitute the naive Bayes model. For the CoIL contest, after two important derived attributes were added, boosting did not give any significant increase ...
... from combinations of existing attributes. Both boosting and derived attributes are ways of relaxing the conditional independence assumptions that constitute the naive Bayes model. For the CoIL contest, after two important derived attributes were added, boosting did not give any significant increase ...
eneralized Partial Global Planning
... Non-spatial-data-dominant Generalization • Ideas: – First step: Attribute-oriented induction. Non-spatial data are generalized at a given level by the threshold. – Second step: Spatial-oriented induction. Merging spatial regions which have the same non-spatial description. Ignore those small region ...
... Non-spatial-data-dominant Generalization • Ideas: – First step: Attribute-oriented induction. Non-spatial data are generalized at a given level by the threshold. – Second step: Spatial-oriented induction. Merging spatial regions which have the same non-spatial description. Ignore those small region ...
“Data Mining Approach for environmental Conditions Assessment of
... • Traditionally these are performed on files • Most of these tasks are much better done inside DB ...
... • Traditionally these are performed on files • Most of these tasks are much better done inside DB ...
Introduction to the Special Issue on Successful Real
... problems and the cause of problems. The problem is important and non-trivial because customer reactions are independent of the internal architecture of the business. In "Market Basket Recommendations for the HP SMB Store" Singh, Thomas and Sepulveda present the application of market basket analysis ...
... problems and the cause of problems. The problem is important and non-trivial because customer reactions are independent of the internal architecture of the business. In "Market Basket Recommendations for the HP SMB Store" Singh, Thomas and Sepulveda present the application of market basket analysis ...
Chapter 8 INTRODUCTION TO SUPERVISED METHODS
... VC-dimension of the inducer is finite. The VC-dimension of a linear classifier is simply the dimension n of the input space, or the number of free parameters of the classifier. The VC-dimension of a general classifier may however be quite different from the number of free parameters and in many case ...
... VC-dimension of the inducer is finite. The VC-dimension of a linear classifier is simply the dimension n of the input space, or the number of free parameters of the classifier. The VC-dimension of a general classifier may however be quite different from the number of free parameters and in many case ...
Advanced Methods to Improve Performance of K
... Abstract - Clustering is an unsupervised classification that is the partitioning of a data set in a set of meaningful subsets. Each object in dataset shares some common property- often proximity according to some defined distance measure. Among various types of clustering techniques, K-Means is one ...
... Abstract - Clustering is an unsupervised classification that is the partitioning of a data set in a set of meaningful subsets. Each object in dataset shares some common property- often proximity according to some defined distance measure. Among various types of clustering techniques, K-Means is one ...
The 6th International Workshop on Multimedia Data Mining(MDM/KDD2005)
... members computed according to various functional versions. This new approach integrates a choice of computation modes of these members into the model, in order to allow the user to choose the best representation of data. In the second paper (“A generalized metric distance between hierarchically part ...
... members computed according to various functional versions. This new approach integrates a choice of computation modes of these members into the model, in order to allow the user to choose the best representation of data. In the second paper (“A generalized metric distance between hierarchically part ...
Analysis of Missing Data and Imputation on Agriculture
... Abstract - Data mining can be defined as the process of selecting, exploring and modeling large amounts of data to uncover previously unknown patterns. Data Mining is emerging research field in Agriculture crop yield analysis. In the present scenario data mining has become the eminent methodology fo ...
... Abstract - Data mining can be defined as the process of selecting, exploring and modeling large amounts of data to uncover previously unknown patterns. Data Mining is emerging research field in Agriculture crop yield analysis. In the present scenario data mining has become the eminent methodology fo ...
paper - Information Engineering Group
... The first remark which can be outlined is that even if the adoption of machine learning algorithm to deal with time oriented data seems meaningful, only few works have been devoted to this problem. However they have been tested onto ad hoc and very simple examples, the focus was into obtaining inter ...
... The first remark which can be outlined is that even if the adoption of machine learning algorithm to deal with time oriented data seems meaningful, only few works have been devoted to this problem. However they have been tested onto ad hoc and very simple examples, the focus was into obtaining inter ...
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.