Some contributions to semi-supervised learning
... or an unsupervised learner in the presence of “side information”. This side information can be in the form of unlabeled samples in the supervised case or pair-wise constraints in the unsupervised case. Most existing semi-supervised learning approaches design a new objective function, which in turn l ...
... or an unsupervised learner in the presence of “side information”. This side information can be in the form of unlabeled samples in the supervised case or pair-wise constraints in the unsupervised case. Most existing semi-supervised learning approaches design a new objective function, which in turn l ...
Oracle Data Mining Case Study: Xerox
... • Wide range of data mining algorithms & statistical functions ...
... • Wide range of data mining algorithms & statistical functions ...
Shashi Shekhar - users.cs.umn.edu
... key assumptions of classical data mining techniques are invalid for geo-spatial data sets. Though classicaldata mining and spatial data mining sharegoals, their domains have different characteristics. First, spatial data is embeddedin a continuous space,whereasclassical data setsare often discrete. ...
... key assumptions of classical data mining techniques are invalid for geo-spatial data sets. Though classicaldata mining and spatial data mining sharegoals, their domains have different characteristics. First, spatial data is embeddedin a continuous space,whereasclassical data setsare often discrete. ...
Ensemble of Classifiers to Improve Accuracy of the CLIP4 Machine
... 1. In phase I positive data is partitioned, using the SC problem, into subsets of similar data. The subsets are stored in a decision-tree like manner, where node of the tree represents one data subset. Each level of the tree is generated using one negative example for building the SC model. The solu ...
... 1. In phase I positive data is partitioned, using the SC problem, into subsets of similar data. The subsets are stored in a decision-tree like manner, where node of the tree represents one data subset. Each level of the tree is generated using one negative example for building the SC model. The solu ...
Ensemble Methods in Data Mining: Improving Accuracy
... ABSTRACT Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges – from inv ...
... ABSTRACT Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges – from inv ...
Data Cleaning: Problems and Current Approaches
... different component structure, different data types, different integrity constraints, etc. In addition to schema-level conflicts, many conflicts appear only at the instance level (data conflicts). All problems from the single-source case can occur with different representations in different sources ...
... different component structure, different data types, different integrity constraints, etc. In addition to schema-level conflicts, many conflicts appear only at the instance level (data conflicts). All problems from the single-source case can occur with different representations in different sources ...
A New Class Based Associative Classification Algorithm
... Zhonghua Tang is with the School of Mathematical Science, South University of Technology, Guangzhou, Guangdong 510640, P. R. ...
... Zhonghua Tang is with the School of Mathematical Science, South University of Technology, Guangzhou, Guangdong 510640, P. R. ...
A Survey on Data Mining Algorithm for Market Basket Analysis
... people purchased collectively. The author provides an innovative market basket analysis technique by mining association rules on the items' internal features that are obtained with the help of automatic words segmentation technique. This technique has been used for dynamic dishes recommend system an ...
... people purchased collectively. The author provides an innovative market basket analysis technique by mining association rules on the items' internal features that are obtained with the help of automatic words segmentation technique. This technique has been used for dynamic dishes recommend system an ...
here
... At the same time, we note that data mining and other forms of data analysis that are being carried out or explored in the counter-terrorism context represent one stage in a series of data-related practices, each of which presents particular issues with respect to privacy, ethics, and human rights. ...
... At the same time, we note that data mining and other forms of data analysis that are being carried out or explored in the counter-terrorism context represent one stage in a series of data-related practices, each of which presents particular issues with respect to privacy, ethics, and human rights. ...
Big Data Technology
... How can we do something useful with such amounts of data? MapReduce Addresses distribution of computation Google’s computational/data manipulation model ...
... How can we do something useful with such amounts of data? MapReduce Addresses distribution of computation Google’s computational/data manipulation model ...
Induction By Attribute Elimination ц
... blood pressure, and temperature are always used to record each patient's medical history and symptoms. Existing data-mining algorithms, such as C4.5 [12] and HCV [15], start with all attributes in a database, and choose useful attributes for concept descriptions. The Rule Induction Two In One (RITIO ...
... blood pressure, and temperature are always used to record each patient's medical history and symptoms. Existing data-mining algorithms, such as C4.5 [12] and HCV [15], start with all attributes in a database, and choose useful attributes for concept descriptions. The Rule Induction Two In One (RITIO ...
Data Monoids
... that maps a word w to its equivalence class under ≡L , is a data monoid homomorphism. This function is called the syntactic morphism. The target of the syntactic morphism is called the syntactic data monoid of L, and denoted ML . Note that data monoid homomorphisms preserve the finite support axiom. ...
... that maps a word w to its equivalence class under ≡L , is a data monoid homomorphism. This function is called the syntactic morphism. The target of the syntactic morphism is called the syntactic data monoid of L, and denoted ML . Note that data monoid homomorphisms preserve the finite support axiom. ...
Survey on Mining Association Rule with Data Structures
... Association rule mining is one of the most significant parts in data mining process. The purpose of association rule mining is to find association relationships or correlations among a set of items. In this paper, a proficient way to discover the legal association rules among the occasionally occur ...
... Association rule mining is one of the most significant parts in data mining process. The purpose of association rule mining is to find association relationships or correlations among a set of items. In this paper, a proficient way to discover the legal association rules among the occasionally occur ...
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.