An Educational Data Mining Approach to Explore The Effect of Using
... taking online quizzes. The classification technique involves training and testing. In training, the data is analyzed through classification algorithms. In testing data, the accuracy will be estimated based on the classification rules (Padmanaban 2014). There are many different classification techniq ...
... taking online quizzes. The classification technique involves training and testing. In training, the data is analyzed through classification algorithms. In testing data, the accuracy will be estimated based on the classification rules (Padmanaban 2014). There are many different classification techniq ...
Automatic Mood Classication of Indian Popular Music
... proposed in the literature for music classification. Different taxonomies exist for the categorization of audio features. Weihs et al. [40] have categorized the audio features into four subcategories, namely short-term features, long-term features, semantic features, and compositional features. Scar ...
... proposed in the literature for music classification. Different taxonomies exist for the categorization of audio features. Weihs et al. [40] have categorized the audio features into four subcategories, namely short-term features, long-term features, semantic features, and compositional features. Scar ...
Chi-square-based Scoring Function for Categorization of MEDLINE
... with the SVM penalty parameter C were optimized by nested cross-validation over d values {1, 2, 3} and C values {0.01, 1, 100} [27]. For each learning algorithm we conducted four experiments with the following inputs for each MEDLINE citation: i) title, ii) abstract, iii) title and abstract, and iv) ...
... with the SVM penalty parameter C were optimized by nested cross-validation over d values {1, 2, 3} and C values {0.01, 1, 100} [27]. For each learning algorithm we conducted four experiments with the following inputs for each MEDLINE citation: i) title, ii) abstract, iii) title and abstract, and iv) ...
Semantic Web in Data Mining and Knowledge Discovery: A
... of the data to a form that data mining algorithms can work on – in most cases, this means turning the data into a propositional form, where each instance is represented by a feature vector. To improve the performance of subsequent data mining algorithms, dimensionality reduction methods can also be ...
... of the data to a form that data mining algorithms can work on – in most cases, this means turning the data into a propositional form, where each instance is represented by a feature vector. To improve the performance of subsequent data mining algorithms, dimensionality reduction methods can also be ...
Association
... c(ABC D) c(AB CD) c(A BCD) Confidence is anti-monotone w.r.t. number of items on the RHS of the rule ...
... c(ABC D) c(AB CD) c(A BCD) Confidence is anti-monotone w.r.t. number of items on the RHS of the rule ...
Paper - Bruno Crémilleux
... accuracy. Two of the most-used concise representations, the free and closed patterns, find their origin in Galois lattice theory and Formal Concept Analysis. A set of patterns is said to form an equivalence class if they are mapped to the same set of objects (or transactions) of a data set, and hence ...
... accuracy. Two of the most-used concise representations, the free and closed patterns, find their origin in Galois lattice theory and Formal Concept Analysis. A set of patterns is said to form an equivalence class if they are mapped to the same set of objects (or transactions) of a data set, and hence ...
A Survey on Issues of Decision Tree and Non-Decision
... bottom-up approach is used for error-based pruning. If the number of predicted errors for the leaf is not greater than the sum of the predicted errors for the leaf nodes of that subtree then subtree is replaced with that leaf [18]. 4.7. Minimum Description Length Pruning Mehata et al., and Quinlan a ...
... bottom-up approach is used for error-based pruning. If the number of predicted errors for the leaf is not greater than the sum of the predicted errors for the leaf nodes of that subtree then subtree is replaced with that leaf [18]. 4.7. Minimum Description Length Pruning Mehata et al., and Quinlan a ...
Pattern Mining and Events Discovery in Molecular Dynamics
... 3.10 Three possible coordination configurations of Si and O atoms are shown. Large spheres are Si atoms and small are O atoms, the simulation box is also marked. The coordination states are color coded: 1(red), 2(yellow), 3(green), 4 (cyan) and 5 (blue). Left: five fold coordinated Si and three fold ...
... 3.10 Three possible coordination configurations of Si and O atoms are shown. Large spheres are Si atoms and small are O atoms, the simulation box is also marked. The coordination states are color coded: 1(red), 2(yellow), 3(green), 4 (cyan) and 5 (blue). Left: five fold coordinated Si and three fold ...
as PDF
... The allure of data mining is that it promises to improve the communication between users and their large volumes of data and allows them to ask of the data complex questions such as: "What has been going on?" or "What are the characteristics of our best customers?" The answer to the first question c ...
... The allure of data mining is that it promises to improve the communication between users and their large volumes of data and allows them to ask of the data complex questions such as: "What has been going on?" or "What are the characteristics of our best customers?" The answer to the first question c ...
A(1)
... – A frequent (k-1)-sequence w1 is merged with another frequent (k-1)-sequence w2 to produce a candidate k-sequence if the subsequence obtained by removing the first event in w1 is the same as the subsequence obtained by removing the last event in w2 The resulting candidate after merging is given by ...
... – A frequent (k-1)-sequence w1 is merged with another frequent (k-1)-sequence w2 to produce a candidate k-sequence if the subsequence obtained by removing the first event in w1 is the same as the subsequence obtained by removing the last event in w2 The resulting candidate after merging is given by ...
When Pattern met Subspace Cluster — a Relationship Story
... In general, in subspace clustering similarity is defined in some relation to subsets or combinations of attributes or dimensions of database objects. Hence, a clustering with n clusters for a database D × A, with the set of objects D and with the full set of attributes A, can be seen as a set C = {( ...
... In general, in subspace clustering similarity is defined in some relation to subsets or combinations of attributes or dimensions of database objects. Hence, a clustering with n clusters for a database D × A, with the set of objects D and with the full set of attributes A, can be seen as a set C = {( ...
Exploiting Data Mining Techniques in the Design of
... 1.2 Integrated use of data mining and data warehousing ................................................ 2 1.3 Unresolved issues and motivation of the thesis ......................................................... 2 1.4 Research challenges considereed to be out of Scope ............................ ...
... 1.2 Integrated use of data mining and data warehousing ................................................ 2 1.3 Unresolved issues and motivation of the thesis ......................................................... 2 1.4 Research challenges considereed to be out of Scope ............................ ...
Rule-Based Data Mining Methods for Classification Problems in
... constructing ensembles of decision trees: Bagging, boosting, and randomization, Machine Learning, 40:139--157, 2000 J. Li, et al. Ensembles of cascading trees, ICDM 2003, pages ...
... constructing ensembles of decision trees: Bagging, boosting, and randomization, Machine Learning, 40:139--157, 2000 J. Li, et al. Ensembles of cascading trees, ICDM 2003, pages ...
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.