An Interactive Data Mining Framework for EarthCube
... Feature extraction is a useful method for reducing the dimensionality of a dataset but it generally works best on data that is regularly gridded. Many geosciences datasets are sparsely populated in space and/or time and hence present significant challenges to the application of traditional feature e ...
... Feature extraction is a useful method for reducing the dimensionality of a dataset but it generally works best on data that is regularly gridded. Many geosciences datasets are sparsely populated in space and/or time and hence present significant challenges to the application of traditional feature e ...
A study of digital mammograms by using clustering algorithms
... attributes). In Weka, for categorical attributes, it uses Hamming distance (distance between two nominal values is 0 if same and 1 if different), whereas studies38,39 uggested that this is not a very accurate distance measure, may be due to poor performance of hierarchical clustering algorithm. To a ...
... attributes). In Weka, for categorical attributes, it uses Hamming distance (distance between two nominal values is 0 if same and 1 if different), whereas studies38,39 uggested that this is not a very accurate distance measure, may be due to poor performance of hierarchical clustering algorithm. To a ...
Sentiment Analysis - Academic Science,International Journal of
... Xi2 and PMI are two different ways of measuring the corelation between the terms and categories. X i2 is better than PMI as it is normalized value; therefore, these values are more comparable across terms in same category. X i2 is used in many applications and one example is contextual advertising. ...
... Xi2 and PMI are two different ways of measuring the corelation between the terms and categories. X i2 is better than PMI as it is normalized value; therefore, these values are more comparable across terms in same category. X i2 is used in many applications and one example is contextual advertising. ...
Interactive HMM construction based on interesting sequences
... Expectation Maximization (EM) algorithm and the process is repeated. The advantage of such an approach is that the new states have clear, user given interpretation. The resulting model is thus understandable, and all hidden states have a clear meaning. This is not possible with automatic methods. Th ...
... Expectation Maximization (EM) algorithm and the process is repeated. The advantage of such an approach is that the new states have clear, user given interpretation. The resulting model is thus understandable, and all hidden states have a clear meaning. This is not possible with automatic methods. Th ...
UNIVERSITY OF CALICUT Department of Computer Science M.Phil. Computer Science
... models for sequential pattern classification, Discrete hidden Markov models, Continous density hidden Markov models. Unit – III Dimension reduction methods, Fisher discriminant analysis, Principal component analysis, Nonparametric techniques for density estimation, Parzen-window method, K-Nearest Ne ...
... models for sequential pattern classification, Discrete hidden Markov models, Continous density hidden Markov models. Unit – III Dimension reduction methods, Fisher discriminant analysis, Principal component analysis, Nonparametric techniques for density estimation, Parzen-window method, K-Nearest Ne ...
a study on educational data mining
... relationships. These models play a key role in building adaptive learning systems in which adaptations or interventions based on the model’s predictions can be used to change what students experience next or even to recommend outside academic services to support their learning [14]. A unique feature ...
... relationships. These models play a key role in building adaptive learning systems in which adaptations or interventions based on the model’s predictions can be used to change what students experience next or even to recommend outside academic services to support their learning [14]. A unique feature ...
Subjectively Interesting Component Analysis: Data Projections that
... Figure 1: Communities data (§1, §4.2), (a) the actual network, (b) nodes colored according to their projected values using the first PCA component, (c) similar to (b), but for the first SICA component (our method). The x-axis corresponds to the first feature in the data, while the position of points ...
... Figure 1: Communities data (§1, §4.2), (a) the actual network, (b) nodes colored according to their projected values using the first PCA component, (c) similar to (b), but for the first SICA component (our method). The x-axis corresponds to the first feature in the data, while the position of points ...
Bayesian Classification: Why? Bayesian Theorem: Basics Bayes
... Weight decay: add penalty term to error function ...
... Weight decay: add penalty term to error function ...
A Comparative Study of Classification and Regression Algorithms
... for classifying students based on their Moodle (e-learning system) usage data and the final marks obtained in their respective programmes. The conclusion was that the most appropriate algorithm was decision trees for being accurate and comprehensible for instructors. Kabakchieva [10] also developed ...
... for classifying students based on their Moodle (e-learning system) usage data and the final marks obtained in their respective programmes. The conclusion was that the most appropriate algorithm was decision trees for being accurate and comprehensible for instructors. Kabakchieva [10] also developed ...
Effective Data Mining for a Transportation Information System
... is a relative frequency of objects that satisfy the succedent at least p×100% higher than the relative frequency of objects satisfying the succedent among all the objects in the whole data matrix; and there are at least B objects that satisfy both antecedent and succedent.” Another way to express th ...
... is a relative frequency of objects that satisfy the succedent at least p×100% higher than the relative frequency of objects satisfying the succedent among all the objects in the whole data matrix; and there are at least B objects that satisfy both antecedent and succedent.” Another way to express th ...
ESDA 201 - ESIP Commons
... through our understanding and implementation of supportive information technologies, in close coordination with the specific data analysis needs of the science community • Data Preparation – Making heterogeneous data so that they can ‘play’ together • Data Reduction – Smartly removing data that do n ...
... through our understanding and implementation of supportive information technologies, in close coordination with the specific data analysis needs of the science community • Data Preparation – Making heterogeneous data so that they can ‘play’ together • Data Reduction – Smartly removing data that do n ...
finding descriptors useful for data mining in the
... examine one (or small n) sample(s) at a time. For example, X-ray powder diffraction scans are collected sequentially, one at a time, on an individual specimen. Each resulting diffraction scan is processed either by hand or in a batch mode for baseline correction, possibly some additional geometric c ...
... examine one (or small n) sample(s) at a time. For example, X-ray powder diffraction scans are collected sequentially, one at a time, on an individual specimen. Each resulting diffraction scan is processed either by hand or in a batch mode for baseline correction, possibly some additional geometric c ...
C5.1.2: Classification methodology
... from a normal distribution Nk (µi , Σi ) where the unknown parameter vector ϑi = (µi , Σi ) comprises the class mean µi ∈ Rk and the covariance matrix Σi of X (for i = 1, ..., m). For discrete data fi (x) is the probability that X takes the value x (in the i-th class). A (non-randomized) decision ru ...
... from a normal distribution Nk (µi , Σi ) where the unknown parameter vector ϑi = (µi , Σi ) comprises the class mean µi ∈ Rk and the covariance matrix Σi of X (for i = 1, ..., m). For discrete data fi (x) is the probability that X takes the value x (in the i-th class). A (non-randomized) decision ru ...
Data Mining for Business
... in a dataset. It arranges variables as sequentially nodes, branching out from a root. At each node the value attributed to the item determine its path. They are very useful when there are clear hierarchies of data. However, they can also be limited in application because they do not allow for any in ...
... in a dataset. It arranges variables as sequentially nodes, branching out from a root. At each node the value attributed to the item determine its path. They are very useful when there are clear hierarchies of data. However, they can also be limited in application because they do not allow for any in ...
CIS 690 (Implementation of High-Performance Data Mining Systems
... – The process of automatically extracting valid, useful, previously unknown, and ultimately comprehensible information from large databases and using it to make crucial business decisions – “Torturing the data until they confess” ...
... – The process of automatically extracting valid, useful, previously unknown, and ultimately comprehensible information from large databases and using it to make crucial business decisions – “Torturing the data until they confess” ...
Data Mining with Neural Networks and Support Vector Machines
... which may require a higher learning curve from the user. Yet, after mastering the R environment, the user achieves a better control (e.g. adaptation to a specific application) and understanding of what is being executed (in contrast with several “black-box” DM GUI products). Nevertheless, for those ...
... which may require a higher learning curve from the user. Yet, after mastering the R environment, the user achieves a better control (e.g. adaptation to a specific application) and understanding of what is being executed (in contrast with several “black-box” DM GUI products). Nevertheless, for those ...
Data Mining - Motivation
... ''show me all association rules with support > 1% and a minimum confidence > 95% that have salary in the head'' ''show me decision trees with estimated accuracy > 90% that are built on at most 5 of the following 7 attributes'' ...
... ''show me all association rules with support > 1% and a minimum confidence > 95% that have salary in the head'' ''show me decision trees with estimated accuracy > 90% that are built on at most 5 of the following 7 attributes'' ...
Soft Computing Techniques and its Impact in Data Mining
... observations and calibration of commonsense rules in an attempt to establish meaningful and useful relationships between system variables. Despite a growing versatility of knowledge discovery systems, there is an important component of human interaction that is inherent to any process of knowledge r ...
... observations and calibration of commonsense rules in an attempt to establish meaningful and useful relationships between system variables. Despite a growing versatility of knowledge discovery systems, there is an important component of human interaction that is inherent to any process of knowledge r ...
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.