BU Bioinformatics seminar
... 1/3 are from the same family with known GI tumor prognostic value 1/3 are X-chromosome testis/cancer-specific antigens 1/2 fall in same cytogenic band, which is also a known CNV hotspot HEFalMp links to a cascade of antigens/membrane receptors/TFs Cell adhesion p-value ≈ 0, moderate correlation in m ...
... 1/3 are from the same family with known GI tumor prognostic value 1/3 are X-chromosome testis/cancer-specific antigens 1/2 fall in same cytogenic band, which is also a known CNV hotspot HEFalMp links to a cascade of antigens/membrane receptors/TFs Cell adhesion p-value ≈ 0, moderate correlation in m ...
THE SMALLEST SET OF CONSTRAINTS THAT EXPLAINS THE
... extreme than in the randomized samples, then we can claim that we have found a significant property of the data. However, this does not necessarily fully explain the data. For example, suppose we are performing k-means clustering. A natural choice would be to use the k-means cost function as the tes ...
... extreme than in the randomized samples, then we can claim that we have found a significant property of the data. However, this does not necessarily fully explain the data. For example, suppose we are performing k-means clustering. A natural choice would be to use the k-means cost function as the tes ...
Support Vector Machines: A Survey
... • One of the initial drawbacks of SVM is its computational inefficiency. • However, this problem is being solved with great success. • SMO: • break a large optimization problem into a series of smaller problems, each only involves a couple of carefully chosen variables • The process iterates ...
... • One of the initial drawbacks of SVM is its computational inefficiency. • However, this problem is being solved with great success. • SMO: • break a large optimization problem into a series of smaller problems, each only involves a couple of carefully chosen variables • The process iterates ...
Support Vector Machines: A Survey
... • One of the initial drawbacks of SVM is its computational inefficiency. • However, this problem is being solved with great success. • SMO: • break a large optimization problem into a series of smaller problems, each only involves a couple of carefully chosen variables • The process iterates ...
... • One of the initial drawbacks of SVM is its computational inefficiency. • However, this problem is being solved with great success. • SMO: • break a large optimization problem into a series of smaller problems, each only involves a couple of carefully chosen variables • The process iterates ...
A Comparative Study on Outlier Detection Techniques
... In this approach, similarity between two objects is measured with the help of distance between the two objects in data space, if this distance exceeds a particular threshold, then the data object will be called as the outlier. There are many algorithms under this category. One of the most popular an ...
... In this approach, similarity between two objects is measured with the help of distance between the two objects in data space, if this distance exceeds a particular threshold, then the data object will be called as the outlier. There are many algorithms under this category. One of the most popular an ...
A Classification Framework based on VPRS Boundary Region using
... following sections). A stopping criterion specifies when to prevent the discretization process. It is usually ruled by a trade-off between lower arity with a better accepting but less accuracy and a higher arity with a poorer belief but higher accuracy. The count of inconsistencies (inconsistency is ...
... following sections). A stopping criterion specifies when to prevent the discretization process. It is usually ruled by a trade-off between lower arity with a better accepting but less accuracy and a higher arity with a poorer belief but higher accuracy. The count of inconsistencies (inconsistency is ...
Data Mining for Knowledge Management Clustering
... Weights should be associated with different variables based on applications and data semantics. It is hard to define “similar enough” or “good enough” ...
... Weights should be associated with different variables based on applications and data semantics. It is hard to define “similar enough” or “good enough” ...
PDF
... In 1985 for content filtering architecture a large scale information system is developed. In 1988 a rule based message filtering system has been proposed after that in 1990 an active mail-filtering agent called as MAFIA for an intelligent document processing support was developed by Lutz, E. R. In t ...
... In 1985 for content filtering architecture a large scale information system is developed. In 1988 a rule based message filtering system has been proposed after that in 1990 an active mail-filtering agent called as MAFIA for an intelligent document processing support was developed by Lutz, E. R. In t ...
Major topics of my research interests
... This has since been applied to various problems, mostly in bioinformatics, several of which are listed below. The algorithm is incorporated in the Matlab program COMPACT that can be downloaded from the Research section of my website. Recently we have developed the Dynamic Quantum Clustering method ( ...
... This has since been applied to various problems, mostly in bioinformatics, several of which are listed below. The algorithm is incorporated in the Matlab program COMPACT that can be downloaded from the Research section of my website. Recently we have developed the Dynamic Quantum Clustering method ( ...
Multiple Features Subset Selection using Meta
... normal classification algorithm is very slow in term of processing time as well as the classification rate is very low. Now these are applying some existing methodology for the improvement of classification of K.N.N algorithm such as MFS [4], MDF [5] and FCMMNC [6]. But all these methods of classifi ...
... normal classification algorithm is very slow in term of processing time as well as the classification rate is very low. Now these are applying some existing methodology for the improvement of classification of K.N.N algorithm such as MFS [4], MDF [5] and FCMMNC [6]. But all these methods of classifi ...
HAP 780 : Data Mining in Health Care - CHHS
... Below are draft instructions for some of the assignments. They are for information purposes only to help students better plan time and understand course content. The actual assignments will be posted on Blackboard and may be different than ones presented here. ...
... Below are draft instructions for some of the assignments. They are for information purposes only to help students better plan time and understand course content. The actual assignments will be posted on Blackboard and may be different than ones presented here. ...
3 The Dynamic Data Mining Approach
... problems, that have faced data mining techniques; that is, database updates, accuracy of data mining results, gaining more knowledge and interpretation of the results, and performance. In this paper, we propose an approach, that dynamically updates knowledge obtained from the previous data mining pr ...
... problems, that have faced data mining techniques; that is, database updates, accuracy of data mining results, gaining more knowledge and interpretation of the results, and performance. In this paper, we propose an approach, that dynamically updates knowledge obtained from the previous data mining pr ...
AN IMPROVED DENSITY BASED k
... would be detected correctly (Hautamaki et al. 2004). This approach performed poorly with very large dataset and high dimensional dataset. It’s mainly used for detecting globular shape cluster. A local outlier factor(LOF) was proposed by Markus et al by assigning a degree to which an object will be r ...
... would be detected correctly (Hautamaki et al. 2004). This approach performed poorly with very large dataset and high dimensional dataset. It’s mainly used for detecting globular shape cluster. A local outlier factor(LOF) was proposed by Markus et al by assigning a degree to which an object will be r ...
Data Mining Originally, data mining was a statistician`s term for
... or show similar characteristics. In common terms it is also called look-a-like groups. • Similarity is often quantified through the use of a distance function. ...
... or show similar characteristics. In common terms it is also called look-a-like groups. • Similarity is often quantified through the use of a distance function. ...
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.