Mining Frequent Patterns with Counting Inference
... patterns in a levelwise manner. During each iteration corresponding to a level, a set of candidate patterns is created by joining the frequent patterns discovered during the previous iteration, the supports of all candidate patterns are counted and infrequent ones are discarded. The most prominent a ...
... patterns in a levelwise manner. During each iteration corresponding to a level, a set of candidate patterns is created by joining the frequent patterns discovered during the previous iteration, the supports of all candidate patterns are counted and infrequent ones are discarded. The most prominent a ...
Calculating Feature Weights in Naive Bayes with Kullback
... has been widely used in many data mining applications, and performs surprisingly well on many applications [2]. However, due to the assumption that all features are equally important in naive Bayesian learning, the predictions estimated by naive Bayesian are sometimes poor. For example, for the prob ...
... has been widely used in many data mining applications, and performs surprisingly well on many applications [2]. However, due to the assumption that all features are equally important in naive Bayesian learning, the predictions estimated by naive Bayesian are sometimes poor. For example, for the prob ...
Mining High Utility Patterns in One Phase without Generating
... De Raedt et al. [14] investigated how standard constraint programming techniques can be applied to constraint-based mining problems with constraints that are monotone, antimonotone, and convertible. Bayardo and Agrawal [5], and Morishita and Sese [31] proposed techniques of pruning based on upper bo ...
... De Raedt et al. [14] investigated how standard constraint programming techniques can be applied to constraint-based mining problems with constraints that are monotone, antimonotone, and convertible. Bayardo and Agrawal [5], and Morishita and Sese [31] proposed techniques of pruning based on upper bo ...
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... An itemset X is closed if X is frequent and there exists no super-pattern Y כX, with the same support as X (proposed by Pasquier, et al. @ ICDT’99) An itemset X is a max-pattern if X is frequent and there exists no frequent super-pattern Y כX (proposed by ...
... An itemset X is closed if X is frequent and there exists no super-pattern Y כX, with the same support as X (proposed by Pasquier, et al. @ ICDT’99) An itemset X is a max-pattern if X is frequent and there exists no frequent super-pattern Y כX (proposed by ...
Improving the Accuracy of Decision Tree Induction by - IBaI
... within the artificial intelligence community are FOCUS ( Almuallim and Diettrich, 1994) and RELIEF (Kira and Rendell, 1992). The FOCUS algorithm starts with an empty feature set and carries out exhaustive search until it finds a minimal combination of features. It works on binary, noise-free data. R ...
... within the artificial intelligence community are FOCUS ( Almuallim and Diettrich, 1994) and RELIEF (Kira and Rendell, 1992). The FOCUS algorithm starts with an empty feature set and carries out exhaustive search until it finds a minimal combination of features. It works on binary, noise-free data. R ...
Knowledge Uncertainty in Intelligent System
... with much needed tools for developing intelligent systems that can handle knowledge uncertainty in a diligent manner. ...
... with much needed tools for developing intelligent systems that can handle knowledge uncertainty in a diligent manner. ...
A biologically-inspired validity measure for comparison - FICH-UNL
... to validate the returned groupings by a clustering algorithm through manual analysis and visual inspection, according to a-priori biological knowledge. In the biological domain, clustering is implemented under the guilt-by-association principle [9], that is to say, the assumption that genes involved ...
... to validate the returned groupings by a clustering algorithm through manual analysis and visual inspection, according to a-priori biological knowledge. In the biological domain, clustering is implemented under the guilt-by-association principle [9], that is to say, the assumption that genes involved ...
Urban Human Mobility Data Mining: An Overview
... and optimizing wireless base station performance. Another example is that knowledge on where the people would visit in a city can be advantageous to both taxi drivers and taxi companies. Taxi drivers can drive to areas where there is a big demand of taxi services if the urban human mobility can be c ...
... and optimizing wireless base station performance. Another example is that knowledge on where the people would visit in a city can be advantageous to both taxi drivers and taxi companies. Taxi drivers can drive to areas where there is a big demand of taxi services if the urban human mobility can be c ...
Three boundary conditions for computing the fixed
... doing this task (in previous work, we found no evidence for two strategies in exactly this example, [11]). At this point, it should be noted that while in many cases variation on behavior is expressed as changes in the response time distributions of the various experimental conditions, this is not a ...
... doing this task (in previous work, we found no evidence for two strategies in exactly this example, [11]). At this point, it should be noted that while in many cases variation on behavior is expressed as changes in the response time distributions of the various experimental conditions, this is not a ...
XML as a Unifying Framework for Inductive Databases
... in the database. They are designed in order to give, to the user/analyst, an intuitive explanation of the underlying laws that guide the customer in his/her purchases. All these different techniques share the goal to extract data patterns from the database, in order to obtain a description that can ...
... in the database. They are designed in order to give, to the user/analyst, an intuitive explanation of the underlying laws that guide the customer in his/her purchases. All these different techniques share the goal to extract data patterns from the database, in order to obtain a description that can ...
Graph-Based Hierarchical Conceptual Clustering
... Figure 2: Graph representation of an animal description. The input graph need not be connected, as is the case when representing unstructured databases. For data represented as feature vectors, instances are often represented as a collection of small, star-like, connected graphs. An example of the r ...
... Figure 2: Graph representation of an animal description. The input graph need not be connected, as is the case when representing unstructured databases. For data represented as feature vectors, instances are often represented as a collection of small, star-like, connected graphs. An example of the r ...
Building Decision Trees with Constraints
... the MDL cost of a subtree. Furthermore, our dynamic programming algorithms are much more efficient than those of Bohanec and Bratko and conceptually much simpler than the “left-to-right” dynamic programming procedure of Almuallim. The problem with such naive approaches is that they essentially apply ...
... the MDL cost of a subtree. Furthermore, our dynamic programming algorithms are much more efficient than those of Bohanec and Bratko and conceptually much simpler than the “left-to-right” dynamic programming procedure of Almuallim. The problem with such naive approaches is that they essentially apply ...
Attribute weighting in K-nearest neighbor classification Muhammad
... be discrete. Classification is also defined as the task of learning a target function that maps each attribute set to one of the predefined class labels. The target function is also called as classification model. Each classification technique employs a learning algorithm to identify a model that be ...
... be discrete. Classification is also defined as the task of learning a target function that maps each attribute set to one of the predefined class labels. The target function is also called as classification model. Each classification technique employs a learning algorithm to identify a model that be ...
Lecture 3
... Instead of matching each transaction against every candidate, match it against candidates contained in the hashed buckets ...
... Instead of matching each transaction against every candidate, match it against candidates contained in the hashed buckets ...
![rastogi02[2]. - Computer Science and Engineering](http://s1.studyres.com/store/data/003484703_1-b6506d8a1f2a07bed0131be804ce5682-300x300.png)
rastogi02[2]. - Computer Science and Engineering
... – Compute Haar (or linear) wavelet transform of C – Coefficient thresholding : only m<
... – Compute Haar (or linear) wavelet transform of C – Coefficient thresholding : only m<
Clustering in applications with multiple data sources—A mutual
... In this paper, we study mining mutual subspace clusters for those applications with multiple data sources. In the clinical and genomic data analysis example, a mutual cluster is a subset of patients that form a cluster in both a subspace of the clinical data source and a subspace of the genomic data ...
... In this paper, we study mining mutual subspace clusters for those applications with multiple data sources. In the clinical and genomic data analysis example, a mutual cluster is a subset of patients that form a cluster in both a subspace of the clinical data source and a subspace of the genomic data ...
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.