chap1_intro-modified
... There is often information “hidden” in the data that is not readily evident Human analysts may take weeks to discover useful information Much of the data is never analyzed at all ...
... There is often information “hidden” in the data that is not readily evident Human analysts may take weeks to discover useful information Much of the data is never analyzed at all ...
8392_S2b - Lyle School of Engineering
... probabilities to example patterns • May not work well with complex tuples in large databases • Fig 3-7, p3-9 CSE 8392 Spring 1999 ...
... probabilities to example patterns • May not work well with complex tuples in large databases • Fig 3-7, p3-9 CSE 8392 Spring 1999 ...
Machine Learning
... Feature(Attribute)-based representations • Examples described by feature(attribute) values – (Boolean, discrete, continuous) ...
... Feature(Attribute)-based representations • Examples described by feature(attribute) values – (Boolean, discrete, continuous) ...
Data warehousing:Data warehousing
... has attractive benefits, an important one being that the individual datasources do not require modification and can continue to function independently. In addition, the architecture of the federated model allows for easy expansion when new datasources become available. The federated approach is less ...
... has attractive benefits, an important one being that the individual datasources do not require modification and can continue to function independently. In addition, the architecture of the federated model allows for easy expansion when new datasources become available. The federated approach is less ...
What is cluster detection?
... • Based on the principle of ratio of mismatched features • For the kth attribute, compute the dissimilarity dk in [0,1] • Set the indicator variable k as follows: – k = 0, if the kth attribute is an asymmetric binary attribute and both objects have value 0 for the attribute – k = 1, otherwise • C ...
... • Based on the principle of ratio of mismatched features • For the kth attribute, compute the dissimilarity dk in [0,1] • Set the indicator variable k as follows: – k = 0, if the kth attribute is an asymmetric binary attribute and both objects have value 0 for the attribute – k = 1, otherwise • C ...
Discovering Interesting Association Rules: A Multi
... presented in [25]. In their work they used the comprehensibility and the interestingness measure of the rules in addition to predictive accuracy. Mata et al. [26] use a GA to optimize the support of an interval for a quantitative attribute. However, their approach is limited to datasets without any ...
... presented in [25]. In their work they used the comprehensibility and the interestingness measure of the rules in addition to predictive accuracy. Mata et al. [26] use a GA to optimize the support of an interval for a quantitative attribute. However, their approach is limited to datasets without any ...
A comparison between statistical and Data Mining methods for credit
... where variables are allowed to enter as continuous, categorical, or ordinal, the formal mechanism by which variable intervals are defined, and they can interact with each other or be restricted to enter in only as additive components. In the second stage, basis functions are removed in the order of ...
... where variables are allowed to enter as continuous, categorical, or ordinal, the formal mechanism by which variable intervals are defined, and they can interact with each other or be restricted to enter in only as additive components. In the second stage, basis functions are removed in the order of ...
A Review of Data Mining as a Tool for Organization`s Growth and
... Applying Data mining in the banking sector can help banks detect patterns of fraudulent credit card use, identify and reward loyal customers, and predicting customers likely to change their credit card affiliation. Similarly, a credit card company can leverage its vast warehouse of customer transact ...
... Applying Data mining in the banking sector can help banks detect patterns of fraudulent credit card use, identify and reward loyal customers, and predicting customers likely to change their credit card affiliation. Similarly, a credit card company can leverage its vast warehouse of customer transact ...
Visualizing Big Datamining: Issues, Challenges and Opportunities
... Data is distinct pieces of information, which is generally formatted in a special way. Data can be classified into two different types namely structured data and unstructured data. Unstructured data refers to information that does not have a predefined or proper data model. Presentations, slide shar ...
... Data is distinct pieces of information, which is generally formatted in a special way. Data can be classified into two different types namely structured data and unstructured data. Unstructured data refers to information that does not have a predefined or proper data model. Presentations, slide shar ...
Data Analysis 2 - Special Clustering algorithms 2
... • A generalization of the grid-based methods. • The grid ranges p for each dimension and density threshold τ . • The τ is the minimum number of points in a regions. • Grid region are detected over a relevant subset of dimensions (traditional methods detect dense regions among all dimension). • Each ...
... • A generalization of the grid-based methods. • The grid ranges p for each dimension and density threshold τ . • The τ is the minimum number of points in a regions. • Grid region are detected over a relevant subset of dimensions (traditional methods detect dense regions among all dimension). • Each ...
Link Mining
... The goal in cluster analysis is to find naturally occurring subclasses. This is done by segmenting the data into groups, where objects in a group are similar to each other and are very dissimilar from objects in different groups. Unlike classification, clustering is unsupervised and can be applied t ...
... The goal in cluster analysis is to find naturally occurring subclasses. This is done by segmenting the data into groups, where objects in a group are similar to each other and are very dissimilar from objects in different groups. Unlike classification, clustering is unsupervised and can be applied t ...
A Wavelet Data Model for Image Databases
... Of the several trends in the development of modern database systems, one of the most interesting is towards the inclusion of data types not contemplated by traditional models and which can, with a certain amount of impropriety, be called multimedia data. From a database point of view, a multimedia o ...
... Of the several trends in the development of modern database systems, one of the most interesting is towards the inclusion of data types not contemplated by traditional models and which can, with a certain amount of impropriety, be called multimedia data. From a database point of view, a multimedia o ...
Function Clustering Self-Organization Maps (FCSOMs - Funpec-RP
... SOMs were first proposed by Kohonen, and were described as unsupervised, selforganizing, self-learning networks, widely used in a variety of clustering analyses. Compared to other clustering methods, SOM networks possess self-stability, can perform clustering processes without evaluations, demonstra ...
... SOMs were first proposed by Kohonen, and were described as unsupervised, selforganizing, self-learning networks, widely used in a variety of clustering analyses. Compared to other clustering methods, SOM networks possess self-stability, can perform clustering processes without evaluations, demonstra ...
Data Mining examples
... ordinal attribute with n values to be coded using n–1 boolean attributes ...
... ordinal attribute with n values to be coded using n–1 boolean attributes ...
data mining - Publishing India Group
... Data Mining in Digital Ambience Digital libraries of textural and multi-media data are now common and will soon be ubiquitous, while digital libraries of numerical data, especially tabular data are growing in importance. In this note, we discuss research challenges arising from the data mining of di ...
... Data Mining in Digital Ambience Digital libraries of textural and multi-media data are now common and will soon be ubiquitous, while digital libraries of numerical data, especially tabular data are growing in importance. In this note, we discuss research challenges arising from the data mining of di ...
Association Rule Generation using Attribute Information Gain and
... Existing classification and rule learning algorithms in machine learning [16] mainly use heuristic/greedy search to find a subset of regularities (e.g., a decision tree or a set of rules) in data for classification[4][5]. In the past few years, extensive research was done in the database community o ...
... Existing classification and rule learning algorithms in machine learning [16] mainly use heuristic/greedy search to find a subset of regularities (e.g., a decision tree or a set of rules) in data for classification[4][5]. In the past few years, extensive research was done in the database community o ...
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.