1 META MINING SYSTEM FOR SUPERVISED LEARNING by
... compactness of user-friendly data models that it generates. These two features make it applicable for applications that use megabytes, or even gigabytes of data. The fields contributing to this research are Inductive Machine Learning, Data Mining and Knowledge Discovery, and Meta Mining. A study of ...
... compactness of user-friendly data models that it generates. These two features make it applicable for applications that use megabytes, or even gigabytes of data. The fields contributing to this research are Inductive Machine Learning, Data Mining and Knowledge Discovery, and Meta Mining. A study of ...
paper
... also a common assumption in certain problem settings that one object can belong to different clusters simultaneously. Surprisingly, although methods in this field have been developed for four decades (starting with [32]), there has not been described a general method of evaluation of clustering result ...
... also a common assumption in certain problem settings that one object can belong to different clusters simultaneously. Surprisingly, although methods in this field have been developed for four decades (starting with [32]), there has not been described a general method of evaluation of clustering result ...
CG33504508
... The goal of clustering is to group the data points or objects that are close or similar to each other and identify such grouping in an unsupervised manner, unsupervised is in the sense that no information is provided to the algorithm about which data point belongs to which cluster. In other words da ...
... The goal of clustering is to group the data points or objects that are close or similar to each other and identify such grouping in an unsupervised manner, unsupervised is in the sense that no information is provided to the algorithm about which data point belongs to which cluster. In other words da ...
Measuring Interestingness–Perspectives on Anomaly Detection
... attempts to answer by stating that "Interestingness depends on the observer's current knowledge and computational abilities. Things are boring if either too much or too little is known about them, if they appear trivial or random." A similar multi-disciplinary construct like interestingness manifest ...
... attempts to answer by stating that "Interestingness depends on the observer's current knowledge and computational abilities. Things are boring if either too much or too little is known about them, if they appear trivial or random." A similar multi-disciplinary construct like interestingness manifest ...
crisp-dm - University of Technology Sydney
... consists of several second-level generic tasks. This second level is called generic, because it is intended to be general enough to cover all possible data mining situations. The generic tasks are intended to be as complete and stable as possible. Complete means covering both the whole process of da ...
... consists of several second-level generic tasks. This second level is called generic, because it is intended to be general enough to cover all possible data mining situations. The generic tasks are intended to be as complete and stable as possible. Complete means covering both the whole process of da ...
New Algorithms for Fast Discovery of Association Rules
... The maximal cliques are discovered using an algorithm similar to the Bierstone's algorithm [19] for generating cliques. For a class [x], and y 2[x], y is said to cover the subset of [x], given by cov(y) = [y] \ [x]. For each class C , we rst identify its covering set, given as fy 2 Cjcov(y) 6= ;; a ...
... The maximal cliques are discovered using an algorithm similar to the Bierstone's algorithm [19] for generating cliques. For a class [x], and y 2[x], y is said to cover the subset of [x], given by cov(y) = [y] \ [x]. For each class C , we rst identify its covering set, given as fy 2 Cjcov(y) 6= ;; a ...
From Local Patterns to Global Models: The LeGo Approach to Data
... necessarily represent exceptions in the data [18], but rather fragmented and incomplete knowledge, which may be fairly general. We identify the following phases: Local Pattern Discovery: This phase is responsible for producing a set of candidate patterns by means of an exploratory analysis of a sear ...
... necessarily represent exceptions in the data [18], but rather fragmented and incomplete knowledge, which may be fairly general. We identify the following phases: Local Pattern Discovery: This phase is responsible for producing a set of candidate patterns by means of an exploratory analysis of a sear ...
Courseware
... are manipulated to perform logic; to reason about the past, and plan for the future; and how the mechanisms of intelligence produce the phenomena of illusion, belief, hope, fear, and dreams-and yes even kindness and love. To understand these functions at a fundamental level, I believe, would be a sc ...
... are manipulated to perform logic; to reason about the past, and plan for the future; and how the mechanisms of intelligence produce the phenomena of illusion, belief, hope, fear, and dreams-and yes even kindness and love. To understand these functions at a fundamental level, I believe, would be a sc ...
Chapter 8 - Jerry Post
... FROM OldRental_ext; INSERT INTO Inventory (ModelID, SKU, ItemSize, QuantityOnHand) SELECT DISTINCT qryOldInventory.ModelID, qryOldInventory.SKU, qryOldInventory.ItemSize, 0 As QuantityOnHand FROM qryOldInventory; Note the use of the column alias to force a zero value for QuantityOnHand for each row ...
... FROM OldRental_ext; INSERT INTO Inventory (ModelID, SKU, ItemSize, QuantityOnHand) SELECT DISTINCT qryOldInventory.ModelID, qryOldInventory.SKU, qryOldInventory.ItemSize, 0 As QuantityOnHand FROM qryOldInventory; Note the use of the column alias to force a zero value for QuantityOnHand for each row ...
PDF
... equally. To differentiate items based on their interest or intensity in [2] the authors focused on discovering more informative association rules. However, weights are introduced only during the rule generation step and were not tailored for infrequent item sets. The pushing of item weights into the ...
... equally. To differentiate items based on their interest or intensity in [2] the authors focused on discovering more informative association rules. However, weights are introduced only during the rule generation step and were not tailored for infrequent item sets. The pushing of item weights into the ...
Discernibility and Rough Sets in Medicine: Tools and
... framework of rough set theory, has been developed. Under the hypothesis that the accessibility of such tools lowers the threshold for abstract ideas to migrate into concrete realization, this aids in reducing a gap between theoreticians and practitioners, and enables existing problems to be more eas ...
... framework of rough set theory, has been developed. Under the hypothesis that the accessibility of such tools lowers the threshold for abstract ideas to migrate into concrete realization, this aids in reducing a gap between theoreticians and practitioners, and enables existing problems to be more eas ...
Extending the Weka Data Mining Toolkit to support Geographic Data
... Table 3 – (left) Feature instance and feature type granularity for high level topological relationships (intersects and non-intersects) and (right) Weka input format.................. 18 Table 4 - (left) Feature instance and feature type granularity for high level for distance relationships and (rig ...
... Table 3 – (left) Feature instance and feature type granularity for high level topological relationships (intersects and non-intersects) and (right) Weka input format.................. 18 Table 4 - (left) Feature instance and feature type granularity for high level for distance relationships and (rig ...
A review of associative classification mining
... from databases (KDD), which extracts useful patterns from data. AC integrates two known data mining tasks, association rule discovery and classification, to build a model (classifier) for the purpose of prediction. Classification and association rule discovery are similar tasks in data mining, with ...
... from databases (KDD), which extracts useful patterns from data. AC integrates two known data mining tasks, association rule discovery and classification, to build a model (classifier) for the purpose of prediction. Classification and association rule discovery are similar tasks in data mining, with ...
A Conceptual Model for Combining Enhanced OLAP and Data
... problem was that the CommonGIS tool lacks the connection to OLAP warehouse to be a complete Business Intelligence (BI) application. They explored how to connect the tool to OLAP warehouses as another source of multi-dimensional data and designed architecture for the extension of CommonGIS. The stren ...
... problem was that the CommonGIS tool lacks the connection to OLAP warehouse to be a complete Business Intelligence (BI) application. They explored how to connect the tool to OLAP warehouses as another source of multi-dimensional data and designed architecture for the extension of CommonGIS. The stren ...
The Application of Data Mining in Crime Prevention: The Case of
... Results of the experiments have shown that decision tree has classified crime records at an accuracy rate of 94 percent when the attribute CrimeLabel is used as a basis for classification. Where as, in the same experiment, the accuracy rate of neural networks is 92.5 percent. On the other hand, in t ...
... Results of the experiments have shown that decision tree has classified crime records at an accuracy rate of 94 percent when the attribute CrimeLabel is used as a basis for classification. Where as, in the same experiment, the accuracy rate of neural networks is 92.5 percent. On the other hand, in t ...
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.