PPT
... Underfitting: when model is too simple, both training and test errors are large © Tan,Steinbach, Kumar ...
... Underfitting: when model is too simple, both training and test errors are large © Tan,Steinbach, Kumar ...
Discretization: An Enabling Technique
... discretization methods were introduced and class information is used to find the proper intervals caused by cut-points. Different methods have been devised to use this class information for finding meaningful intervals in continuous attributes. Supervised and unsupervised discretization have their d ...
... discretization methods were introduced and class information is used to find the proper intervals caused by cut-points. Different methods have been devised to use this class information for finding meaningful intervals in continuous attributes. Supervised and unsupervised discretization have their d ...
Oracle Data Mining Application Developer`s Guide
... inspiration to all who worked on this release. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you ...
... inspiration to all who worked on this release. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you ...
Explaining Data Patterns using Knowledge from the Web of Data
... Knowledge Discovery (KD) is a long-tradition field aiming at developing methodologies to detect hidden patterns and regularities in large datasets, using techniques from a wide range of domains, such as statistics, machine learning, pattern recognition or data visualisation. In most real world conte ...
... Knowledge Discovery (KD) is a long-tradition field aiming at developing methodologies to detect hidden patterns and regularities in large datasets, using techniques from a wide range of domains, such as statistics, machine learning, pattern recognition or data visualisation. In most real world conte ...
Business Intelligence Certification Guide
... software-based test that is non-platform, non-product specific, for use by consultants and implementors. The core requirement for this certification consists of two tests: Test 503, DB2 UDB V5 Fundamentals or Test 509, DB2 UDB V6.1 Fundamentals, and Test 515, Business Intelligence Solutions. For the ...
... software-based test that is non-platform, non-product specific, for use by consultants and implementors. The core requirement for this certification consists of two tests: Test 503, DB2 UDB V5 Fundamentals or Test 509, DB2 UDB V6.1 Fundamentals, and Test 515, Business Intelligence Solutions. For the ...
Temporal Data Mining in Electronic Medical Records from Patients
... events, i.e. clinical practice patterns (CPP). Using the SPM in the ACSPD, I discovered 39 order sets. Not all order sets are present for the 9 year span and overall order set use drops in 2004. I postulate that this denotes a shift in medical practice. In late 2004, the American Heart Association ( ...
... events, i.e. clinical practice patterns (CPP). Using the SPM in the ACSPD, I discovered 39 order sets. Not all order sets are present for the 9 year span and overall order set use drops in 2004. I postulate that this denotes a shift in medical practice. In late 2004, the American Heart Association ( ...
Cluster Analysis: Basic Concepts and Methods
... Partitioning methods: Given a set of n objects, a partitioning method constructs k partitions of the data, where each partition represents a cluster and k ≤ n. That is, it divides the data into k groups such that each group must contain at least one object. In other words, partitioning methods condu ...
... Partitioning methods: Given a set of n objects, a partitioning method constructs k partitions of the data, where each partition represents a cluster and k ≤ n. That is, it divides the data into k groups such that each group must contain at least one object. In other words, partitioning methods condu ...
An R Package for Determining the Relevant Number of Clusters in a
... clusters, the density of clusters or, at least, the number of points in a cluster. Nonhierarchical procedures usually require the user to specify the number of clusters before any clustering is accomplished and hierarchical methods routinely produce a series of solutions ranging from n clusters to a ...
... clusters, the density of clusters or, at least, the number of points in a cluster. Nonhierarchical procedures usually require the user to specify the number of clusters before any clustering is accomplished and hierarchical methods routinely produce a series of solutions ranging from n clusters to a ...
DEVELOPING INTELLIGENT SYSTEMS FOR
... research goals. During my career as a PhD research assistant, he was always prepared to give me advice and support (even when his schedule was very busy), and the many discussions we had were both very interesting and stimulating. Professor Vanthienen taught me that data mining is not only about com ...
... research goals. During my career as a PhD research assistant, he was always prepared to give me advice and support (even when his schedule was very busy), and the many discussions we had were both very interesting and stimulating. Professor Vanthienen taught me that data mining is not only about com ...
Text Mining Infrastructure in R
... popularity of XML based formats (e.g., RDF/XML as a common representation for RDF) tools need to be able to handle XML documents and metadata. The benefit of text mining comes with the large amount of valuable information latent in texts which is not available in classical structured data formats fo ...
... popularity of XML based formats (e.g., RDF/XML as a common representation for RDF) tools need to be able to handle XML documents and metadata. The benefit of text mining comes with the large amount of valuable information latent in texts which is not available in classical structured data formats fo ...
Nearest Neighbour - University of Houston
... • Surprisingly, many UCI datasets can be compressed by just using a single representative per class without a significant loss in accuracy. • SCE tends to pick representatives that are in the center of a region that is dominated by a single class; it removes examples that are classified correctly as ...
... • Surprisingly, many UCI datasets can be compressed by just using a single representative per class without a significant loss in accuracy. • SCE tends to pick representatives that are in the center of a region that is dominated by a single class; it removes examples that are classified correctly as ...
etd-0704103-082302æ
... and the second is minimum confidence. A large itemset is the itemset that satisfies the minimum support. A strong association rule is a large itemset which is in the form of "A Æ B" and satisfies the minimum confidence. The support of an itemset is the fraction of transactions that contain the items ...
... and the second is minimum confidence. A large itemset is the itemset that satisfies the minimum support. A strong association rule is a large itemset which is in the form of "A Æ B" and satisfies the minimum confidence. The support of an itemset is the fraction of transactions that contain the items ...
Trends in Spatial Data Mining - users.cs.umn.edu
... kernel functions from the observed values in the training dataset. For reliable estimates, even larger training datasets are needed relative to those needed for the Bayesian classifiers without spatial context, since we are estimating a more complex distribution. An assumption on P r(X|li , Li ) may ...
... kernel functions from the observed values in the training dataset. For reliable estimates, even larger training datasets are needed relative to those needed for the Bayesian classifiers without spatial context, since we are estimating a more complex distribution. An assumption on P r(X|li , Li ) may ...
UNIT-4 Data Mining Basics
... applying data mining to the sales trends’ data over some months or years. • Data mining discovers relationships of this type. The relationships may be between two or more different objects along with the time dimension or between the attributes of the same object. • Discovery of knowledge is a key r ...
... applying data mining to the sales trends’ data over some months or years. • Data mining discovers relationships of this type. The relationships may be between two or more different objects along with the time dimension or between the attributes of the same object. • Discovery of knowledge is a key r ...
Automatic Document Topic Identification Using Hierarchical
... around the world has led to a greatly increased need for machine understanding of their topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. We introduce in this thesis a novel approach for identifying document topics. In ...
... around the world has led to a greatly increased need for machine understanding of their topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. We introduce in this thesis a novel approach for identifying document topics. In ...
Early Classification on Time Series
... accuracy of elastic measures converges with Euclidean distance. In this paper, we focus on extending the 1NN classifier with the Euclidean distance to achieve early classification. However, our principle can be applied to other instance-based methods using different distance metrics. This paper is a ...
... accuracy of elastic measures converges with Euclidean distance. In this paper, we focus on extending the 1NN classifier with the Euclidean distance to achieve early classification. However, our principle can be applied to other instance-based methods using different distance metrics. This paper is a ...
Computational Geometry and Spatial Data Mining
... • Flock and meet patterns require algorithms in 3dimensional space (space-time) • Exact algorithms are inefficient only suitable for smaller data sets • Approximation can reduce running time with one or two orders of magnitude ...
... • Flock and meet patterns require algorithms in 3dimensional space (space-time) • Exact algorithms are inefficient only suitable for smaller data sets • Approximation can reduce running time with one or two orders of magnitude ...
Case Studies in Data Mining
... 3.2. A small excerpt of the resulting ExampleSet. ............................................................................ 17 3.3. Metadata of the resulting ExampleSet. ...................................................................................... 18 3.4. A small excerpt of the resulting ...
... 3.2. A small excerpt of the resulting ExampleSet. ............................................................................ 17 3.3. Metadata of the resulting ExampleSet. ...................................................................................... 18 3.4. A small excerpt of the resulting ...
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.