Graph-based Educational Data Mining (G-EDM 2015) - CEUR
... The goal in much of this work is to identify rules that can be used to characterize good and poor interactions or good and poor graphs. Xue at al. sought address this challenge in part via the automatic induction of graph rules for student-produced diagrams [22]. In their ongoing work they are apply ...
... The goal in much of this work is to identify rules that can be used to characterize good and poor interactions or good and poor graphs. Xue at al. sought address this challenge in part via the automatic induction of graph rules for student-produced diagrams [22]. In their ongoing work they are apply ...
View PDF - CiteSeerX
... perhaps in dollars saved due to better known changes, as well as decidpredictions or speed-up in a system’s ing DBMS issues, such as data problem (asking response time). Such notions as novtypes, schema, and mapping of elty and understandability are much missing and unknown values more subjective. I ...
... perhaps in dollars saved due to better known changes, as well as decidpredictions or speed-up in a system’s ing DBMS issues, such as data problem (asking response time). Such notions as novtypes, schema, and mapping of elty and understandability are much missing and unknown values more subjective. I ...
The KDD process for extracting useful knowledge from volumes of
... perhaps in dollars saved due to better known changes, as well as decidpredictions or speed-up in a system’s ing DBMS issues, such as data problem (asking response time). Such notions as novtypes, schema, and mapping of elty and understandability are much missing and unknown values more subjective. I ...
... perhaps in dollars saved due to better known changes, as well as decidpredictions or speed-up in a system’s ing DBMS issues, such as data problem (asking response time). Such notions as novtypes, schema, and mapping of elty and understandability are much missing and unknown values more subjective. I ...
Title of Talk
... if OD280/D315 > 2.505 proline > 726.5 color > 3.435 then class 1 if OD280/D315 > 2.505 proline > 726.5 color < 3.435 then class 2 if OD280/D315 < 2.505 hue > 0.875 malic-acid < 2.82 then class 2 if OD280/D315 > 2.505 proline < 726.5 then class 2 if OD280/D315 < 2.505 hue < 0.875 then ...
... if OD280/D315 > 2.505 proline > 726.5 color > 3.435 then class 1 if OD280/D315 > 2.505 proline > 726.5 color < 3.435 then class 2 if OD280/D315 < 2.505 hue > 0.875 malic-acid < 2.82 then class 2 if OD280/D315 > 2.505 proline < 726.5 then class 2 if OD280/D315 < 2.505 hue < 0.875 then ...
Data Mining Smart Energy Time Series
... of this task is to find every sequence that appears repeatedly in a time series. The sequence can be known from the beginning or not. Given a sequence as pattern, this technique performs a search to find other sequences that are similar with the pattern, but the search for unknown motifs is a more c ...
... of this task is to find every sequence that appears repeatedly in a time series. The sequence can be known from the beginning or not. Given a sequence as pattern, this technique performs a search to find other sequences that are similar with the pattern, but the search for unknown motifs is a more c ...
Course Content What is an Outlier?
... • DB(p,d) outliers tend to be points that lie in the sparse regions of the feature space and they are identified on the basis of the nearest neighbour density estimation. The range of neighborhood is set using parameters p (density) and d (radius). • If neighbours lie relatively far, then the point ...
... • DB(p,d) outliers tend to be points that lie in the sparse regions of the feature space and they are identified on the basis of the nearest neighbour density estimation. The range of neighborhood is set using parameters p (density) and d (radius). • If neighbours lie relatively far, then the point ...
Automated Data Mining, Error Analysis, and Reporting
... The Forest Inventory and Analysis (FIA) program of the U.S. Department of Agriculture Forest Service conducts comprehensive forest inventories to estimate the area, volume, growth, and removal of forest resources in the United States, in addition to taking measurements on the health and condition of ...
... The Forest Inventory and Analysis (FIA) program of the U.S. Department of Agriculture Forest Service conducts comprehensive forest inventories to estimate the area, volume, growth, and removal of forest resources in the United States, in addition to taking measurements on the health and condition of ...
Data Preprocessing - Baylor University
... Process to obtain a reduced representation of a data set, which is much smaller in volume but produces almost the same analytical results ...
... Process to obtain a reduced representation of a data set, which is much smaller in volume but produces almost the same analytical results ...
An integrated platform for spatial data mining and
... found a spatial cluster or interesting classification, using either the interactive approach of Descartes or the automated search of GAM. What attributes are associated with a cluster that could potentially explain it? To answer this question, spatial data mining methods are applied. The key to spat ...
... found a spatial cluster or interesting classification, using either the interactive approach of Descartes or the automated search of GAM. What attributes are associated with a cluster that could potentially explain it? To answer this question, spatial data mining methods are applied. The key to spat ...
How to Use the Fractal Dimension to Find Correlations - ICMC
... sets can largely be well-approximated using fewer dimensions. Moreover, attribute selection can also be seen as a way to compress data, since only the attributes which maintain the essential characteristics of the data are kept [7]. Let $={a1, a2,...an} denote a relation and ai its attributes. So, a ...
... sets can largely be well-approximated using fewer dimensions. Moreover, attribute selection can also be seen as a way to compress data, since only the attributes which maintain the essential characteristics of the data are kept [7]. Let $={a1, a2,...an} denote a relation and ai its attributes. So, a ...
Data Transformation - Iust personal webpages
... A relational database or a dimension location of a data warehouse may contain the following group of attributes: street, city, province or state, and country. A user or expert can easily define a concept hierarchy by specifying ordering of the attributes at the schema level. A hierarchy can be d ...
... A relational database or a dimension location of a data warehouse may contain the following group of attributes: street, city, province or state, and country. A user or expert can easily define a concept hierarchy by specifying ordering of the attributes at the schema level. A hierarchy can be d ...
![[Full Text] - International Journal of Computer Science](http://s1.studyres.com/store/data/007742952_2-162e9ea2f18bda18acadf770116f257c-300x300.png)
Data Preprocessing for Supervised Leaning
... eliminating mislabelled instances prior to applying the chosen ML algorithm. Their first step is to identify candidate instances by using m learning algorithms to tag instances as correctly or incorrectly labelled. The second step is to form a classifier using a new version of the training data for ...
... eliminating mislabelled instances prior to applying the chosen ML algorithm. Their first step is to identify candidate instances by using m learning algorithms to tag instances as correctly or incorrectly labelled. The second step is to form a classifier using a new version of the training data for ...
A Mediator to Integrate Databases and Legacy Systems: The
... extremely important for executives to be able to obtain one unique view of information in an accurate and timely manner. To do this, it is necessary to interoperate multiple data sources, which differ structurally and semantically. In the process of interoperating any two or more database systems, t ...
... extremely important for executives to be able to obtain one unique view of information in an accurate and timely manner. To do this, it is necessary to interoperate multiple data sources, which differ structurally and semantically. In the process of interoperating any two or more database systems, t ...
Subspace Clustering for High Dimensional Categorical
... becomes almost the same [5], therefore it is difficult to differentiate similar data points from dissimilar ones. Secondly, clusters are embedded in the subspaces of the high dimensional data space, and different clusters may exist in different subspaces of different dimensions [3]. Because of these ...
... becomes almost the same [5], therefore it is difficult to differentiate similar data points from dissimilar ones. Secondly, clusters are embedded in the subspaces of the high dimensional data space, and different clusters may exist in different subspaces of different dimensions [3]. Because of these ...
Ontology-based multimedia data mining for design information
... We consider that the KD process in multimedia design data has to take in account the underlying information model. Unfortunately, there are numerous information models. The "product modeling" approach is one of the most popular streams. The aim of "product modeling" projects like STEP (Wilson, 1993) ...
... We consider that the KD process in multimedia design data has to take in account the underlying information model. Unfortunately, there are numerous information models. The "product modeling" approach is one of the most popular streams. The aim of "product modeling" projects like STEP (Wilson, 1993) ...
assoc - CSE, IIT Bombay
... Improve predictive capability of classifiers that assume attribute independence Improved clustering of categorical attributes ...
... Improve predictive capability of classifiers that assume attribute independence Improved clustering of categorical attributes ...
Ontology-based multimedia data mining for design
... We consider that the KD process in multimedia design data has to take in account the underlying information model. Unfortunately, there are numerous information models. The "product modeling" approach is one of the most popular streams. The aim of "product modeling" projects like STEP (Wilson, 1993) ...
... We consider that the KD process in multimedia design data has to take in account the underlying information model. Unfortunately, there are numerous information models. The "product modeling" approach is one of the most popular streams. The aim of "product modeling" projects like STEP (Wilson, 1993) ...
Knowledge Discovery and Data Mining: Concepts and Fundamental
... can get newly and unseen samples and is able to predict values of one or more variables related to the sample. However, some prediction-oriented methods can also help provide understanding of the data. Most of the discovery-oriented techniques are based on inductive learning [Mitchell (1997)], where ...
... can get newly and unseen samples and is able to predict values of one or more variables related to the sample. However, some prediction-oriented methods can also help provide understanding of the data. Most of the discovery-oriented techniques are based on inductive learning [Mitchell (1997)], where ...
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.