COMP1942
... Divisive methods – polythetic approach and monothetic approach How to use the data mining tool ...
... Divisive methods – polythetic approach and monothetic approach How to use the data mining tool ...
Complementary Analysis of High-Order Association Patterns and Classification
... 3.3 Classifier Construction To construct a classifier is to extract a subset of high quality patterns that can represent the training dataset. To achieve this goal, filtering out patterns that confuse between the classes or overfit the data is required. Specific patterns with a large number of value ...
... 3.3 Classifier Construction To construct a classifier is to extract a subset of high quality patterns that can represent the training dataset. To achieve this goal, filtering out patterns that confuse between the classes or overfit the data is required. Specific patterns with a large number of value ...
Third-Generation Data Mining: Towards Service
... user to maintain the relationship in the light of changes. Another weak point is that workflows are not checked for correctness before execution: it frequently happens that the execution of the workflow stops with an error after several hours runtime because of small syntactic incompatibilities betw ...
... user to maintain the relationship in the light of changes. Another weak point is that workflows are not checked for correctness before execution: it frequently happens that the execution of the workflow stops with an error after several hours runtime because of small syntactic incompatibilities betw ...
Mining Temporal Patterns for Interval-Based and Point-Based
... In this paper, a new algorithm Hybrid TPrefixSpan is proposed and implemented. This technique uses the approach of Tprefix span and extends the algorithm by including point based events also in the temporal sequence. Thus the proposed algorithm can be used for both interval-based and point-based eve ...
... In this paper, a new algorithm Hybrid TPrefixSpan is proposed and implemented. This technique uses the approach of Tprefix span and extends the algorithm by including point based events also in the temporal sequence. Thus the proposed algorithm can be used for both interval-based and point-based eve ...
DM-6 Updated - Computer Science Unplugged
... Which Attribute is the Best Classifier?: Information Gain The information gain obtained by separating the examples according to the attribute Wind is calculated as: ...
... Which Attribute is the Best Classifier?: Information Gain The information gain obtained by separating the examples according to the attribute Wind is calculated as: ...
Data Mining and Official Statistics
... research institutes as mathematical statisticians and academics. This distinction provides a second reason that warrants the application of data mining to official data as there exists an opposing view between blue-collar statisticians and white-collar statisticians because of the existence of probl ...
... research institutes as mathematical statisticians and academics. This distinction provides a second reason that warrants the application of data mining to official data as there exists an opposing view between blue-collar statisticians and white-collar statisticians because of the existence of probl ...
Feature Selection
... microarray analysis, mass spectrum analysis, sequence analysis, and so on. Text Clustering The task of text clustering is to group similar documents together. In text clustering, a text or document is always represented as a bag of words, which causes high-dimensional feature space and sparse repres ...
... microarray analysis, mass spectrum analysis, sequence analysis, and so on. Text Clustering The task of text clustering is to group similar documents together. In text clustering, a text or document is always represented as a bag of words, which causes high-dimensional feature space and sparse repres ...
Scoring the Data Using Association Rules
... (or error rate) is commonly used as the measure to evaluate classifiers. However, for scoring, this is inadequate. The reasons are as follows: 1. Classification accuracy measures the percentage of data cases that are classified correctly (or wrongly) by a classifier. It cannot be used to evaluate th ...
... (or error rate) is commonly used as the measure to evaluate classifiers. However, for scoring, this is inadequate. The reasons are as follows: 1. Classification accuracy measures the percentage of data cases that are classified correctly (or wrongly) by a classifier. It cannot be used to evaluate th ...
A survey on Data Mining: Tools, Techniques, Applications, Trends
... emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. Data mining attempts to formulate analyze and implement basic induction processes that facilitate the extraction of meaningful information and knowledge from unstructu ...
... emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. Data mining attempts to formulate analyze and implement basic induction processes that facilitate the extraction of meaningful information and knowledge from unstructu ...
WEKA Overview
... WEKA Classification – Naïve Bayes Example • Naïve Bayes is a probabilistic classifier using Bayes’ theorem. • Assumes that the value of features are independent of other features and that features have equal importance. • Hence “Naïve” ...
... WEKA Classification – Naïve Bayes Example • Naïve Bayes is a probabilistic classifier using Bayes’ theorem. • Assumes that the value of features are independent of other features and that features have equal importance. • Hence “Naïve” ...
Meta-Learning Rule Learning Heuristics
... wide variety of rule evaluation metrics were analyzed and compared by visualizing their behavior in ROC space. There is some work on introducing new heuristics but all of them were found under the condition of a fixed bias. For example, in [7] they adjusted parameters of three heuristics, whose shap ...
... wide variety of rule evaluation metrics were analyzed and compared by visualizing their behavior in ROC space. There is some work on introducing new heuristics but all of them were found under the condition of a fixed bias. For example, in [7] they adjusted parameters of three heuristics, whose shap ...
Spatial outlier detection based on iterative self
... standard statistical distribution models. Some representative distribution models like Gaussian or Poisson are frequently used to identify outliers that perform irregularly in such models [4]. In clustering-based approaches, the identification of outliers is normally considered as a side product whil ...
... standard statistical distribution models. Some representative distribution models like Gaussian or Poisson are frequently used to identify outliers that perform irregularly in such models [4]. In clustering-based approaches, the identification of outliers is normally considered as a side product whil ...
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.