Classification in the Presence of Background Domain Knowledge
... more concise models by working at different levels of abstraction and exploring the relationship between concepts in the data set. The main contributions of this work are: 1. a concept hierarchy guided decision tree learning algorithm, that is able to take advantage of user supplied feature (attribu ...
... more concise models by working at different levels of abstraction and exploring the relationship between concepts in the data set. The main contributions of this work are: 1. a concept hierarchy guided decision tree learning algorithm, that is able to take advantage of user supplied feature (attribu ...
Clustering of time series data—a survey
... time series data are concerned, distinctions can be made as to whether the data are discrete-valued or real-valued, uniformly or non-uniformly sampled, univariate or multivariate, and whether data series are of equal or unequal length. Non-uniformly sampled data must be converted into uniformed data ...
... time series data are concerned, distinctions can be made as to whether the data are discrete-valued or real-valued, uniformly or non-uniformly sampled, univariate or multivariate, and whether data series are of equal or unequal length. Non-uniformly sampled data must be converted into uniformed data ...
Solving Complex Machine Learning Problems with Ensemble Methods
... was to discuss ensemble strategies that not only focused on supervised classification, but that could be used to solve difficult and general machine learning problems. The workshop brought together members of the ensemble methods community and also researchers from other fields that could benefit fr ...
... was to discuss ensemble strategies that not only focused on supervised classification, but that could be used to solve difficult and general machine learning problems. The workshop brought together members of the ensemble methods community and also researchers from other fields that could benefit fr ...
The GC3 framework : grid density based clustering for
... system and as such will not be totally reliable. Also the rate at which this data is being generated (real time in many cases) is much higher than the rate at which it can be analyzed by traditional data mining techniques. In such a dynamic environment, the basic tasks of Data mining such as Cluster ...
... system and as such will not be totally reliable. Also the rate at which this data is being generated (real time in many cases) is much higher than the rate at which it can be analyzed by traditional data mining techniques. In such a dynamic environment, the basic tasks of Data mining such as Cluster ...
PDF version
... One of the applications areas of data mining is World Wide Web (WWW), which serves as a huge, widely distributed, global information service center for every kind of information such as news, advertisements, consumer information, financial management, education, government, e-commerce, health servic ...
... One of the applications areas of data mining is World Wide Web (WWW), which serves as a huge, widely distributed, global information service center for every kind of information such as news, advertisements, consumer information, financial management, education, government, e-commerce, health servic ...
Deep learning is not the panacea - Computer Science | CU
... into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to estimating student proficiency, termed deep knowledge tracing or DKT [17], has demonstrated a stunning performance advantage over the main ...
... into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to estimating student proficiency, termed deep knowledge tracing or DKT [17], has demonstrated a stunning performance advantage over the main ...
chapter 6 data mining
... It is common to have observations with missing values for one or more variables. The primary options for addressing missing data are: (1) discard observations with any missing values, (2) discard variable(s) with missing values, (3) fill-in missing entries with estimated values, or (4) apply a data ...
... It is common to have observations with missing values for one or more variables. The primary options for addressing missing data are: (1) discard observations with any missing values, (2) discard variable(s) with missing values, (3) fill-in missing entries with estimated values, or (4) apply a data ...
Ensemble Learning Techniques for Structured
... classification models such as decision trees, artificial neural networks, Naïve Bayes, as well as many other classifiers (Kim, 2009). Ensemble learning, based on aggregating the results from multiple models, is a more sophisticated approach for increasing model accuracy as compared to the traditiona ...
... classification models such as decision trees, artificial neural networks, Naïve Bayes, as well as many other classifiers (Kim, 2009). Ensemble learning, based on aggregating the results from multiple models, is a more sophisticated approach for increasing model accuracy as compared to the traditiona ...
Application and evaluation of inductive reasoning methods for the
... applied to draw conclusions about an individual given some statistical quantities such as probabilities, averages, or deviations from a previous examined population. In other words, by the use of statistical induction techniques, additional triples are derived based on some (precomputed) statistics ...
... applied to draw conclusions about an individual given some statistical quantities such as probabilities, averages, or deviations from a previous examined population. In other words, by the use of statistical induction techniques, additional triples are derived based on some (precomputed) statistics ...
Package `RODM`
... specified (or defaults to an algorithm-specific model name). When created, the model will exist in Oracle as a database schema object. Most algorithms accept a parameter to direct ODM to enable automatic data preparation (default TRUE). This feature will request that ODM prepare data as befitting in ...
... specified (or defaults to an algorithm-specific model name). When created, the model will exist in Oracle as a database schema object. Most algorithms accept a parameter to direct ODM to enable automatic data preparation (default TRUE). This feature will request that ODM prepare data as befitting in ...
To Explain or to Predict? - Department of Statistics
... is often valued for its applied utility, yet is discarded for scientific purposes such as theory building or testing. Shmueli and Koppius (2010) illustrated the lack of predictive modeling in the field of IS. Searching the 1072 papers published in the two top-rated journals Information Systems Resea ...
... is often valued for its applied utility, yet is discarded for scientific purposes such as theory building or testing. Shmueli and Koppius (2010) illustrated the lack of predictive modeling in the field of IS. Searching the 1072 papers published in the two top-rated journals Information Systems Resea ...