The 2016 (12th) International Conference on Data Mining
... workshops, and symposiums into a coordinated research meeting held in a common place at a common time. This model facilitates communication among researchers in different fields of computer science, computer engineering, and applied computing. The Congress also encourages multi-disciplinary and inte ...
... workshops, and symposiums into a coordinated research meeting held in a common place at a common time. This model facilitates communication among researchers in different fields of computer science, computer engineering, and applied computing. The Congress also encourages multi-disciplinary and inte ...
Chapter 12 - KSU Web Home
... database that contains a subset of corporate data to support the analytical requirements of a particular business unit (such as the Sales department) or to support users who share the same requirements to analyse a particular ...
... database that contains a subset of corporate data to support the analytical requirements of a particular business unit (such as the Sales department) or to support users who share the same requirements to analyse a particular ...
Introduction to Spatial Data Mining Spatial Data mining
... should capture the natural structure of the data. y Clusteringg for utilityy (be ( useful): ) cluster analysis y provides p an ...
... should capture the natural structure of the data. y Clusteringg for utilityy (be ( useful): ) cluster analysis y provides p an ...
Stella - Computer Science, Columbia University
... classifiers. Clustering is the second type of ML. It is similar to Classification Learning, but the attribute to predict will be defined by the model itself (rather than the user). By doing that, the model will be able to group similar classes (the class represents the relationship between predictor ...
... classifiers. Clustering is the second type of ML. It is similar to Classification Learning, but the attribute to predict will be defined by the model itself (rather than the user). By doing that, the model will be able to group similar classes (the class represents the relationship between predictor ...
OUTLIER DETECTION USING ENHANCED K
... distance() function that is used to calculate the distance between data object and its nearest cluster head. Next, the distance_ new() function can be used to calculate distance between data objects and other remaining clusters. The experimental results demonstrate that the proposed k-means clusteri ...
... distance() function that is used to calculate the distance between data object and its nearest cluster head. Next, the distance_ new() function can be used to calculate distance between data objects and other remaining clusters. The experimental results demonstrate that the proposed k-means clusteri ...
Decentralized Jointly Sparse Optimization by Reweighted Lq
... Convex: with global convergence guarantee Nonconvex: often with better recovery performance Look back on nonconvex models to recover a single sparse signal Reweighted L1/L2 norm minimization [4][5] Reweighted algorithms for jointly sparse optimization? ...
... Convex: with global convergence guarantee Nonconvex: often with better recovery performance Look back on nonconvex models to recover a single sparse signal Reweighted L1/L2 norm minimization [4][5] Reweighted algorithms for jointly sparse optimization? ...
Data Visualization INFO-GB 3306.10 (9/17 - 12/10)
... This is a blended online course. We will meet 9 times in the classroom. During the weeks of 10/15, 10/29, and 11/5 there will be no in-class meetings. Instead on 10/15 there will be an “remote online live web conference” with a Tableau expert. On 10/29 and 11/5 you’ll work on learning how best to us ...
... This is a blended online course. We will meet 9 times in the classroom. During the weeks of 10/15, 10/29, and 11/5 there will be no in-class meetings. Instead on 10/15 there will be an “remote online live web conference” with a Tableau expert. On 10/29 and 11/5 you’ll work on learning how best to us ...
Relational Data Mining with Inductive Logic Programming for Link
... each classifier is a logical theory generated by Aleph. Many methods have been presented for ensemble generation [10]. In this paper, we concentrate on a popular method that is known to frequently create a more accurate ensemble than individual components, bagging [1]. Bagging works by training each ...
... each classifier is a logical theory generated by Aleph. Many methods have been presented for ensemble generation [10]. In this paper, we concentrate on a popular method that is known to frequently create a more accurate ensemble than individual components, bagging [1]. Bagging works by training each ...
On the Relationship Between Feature Selection and Classification
... As the dimensionality of the data increases, many types of data analysis and classification problems become significantly harder. Sometimes the data also becomes increasingly sparse in the space it occupies. This can lead to big problems for both supervised and unsupervised learning. In the literatu ...
... As the dimensionality of the data increases, many types of data analysis and classification problems become significantly harder. Sometimes the data also becomes increasingly sparse in the space it occupies. This can lead to big problems for both supervised and unsupervised learning. In the literatu ...
A Survey on Association Rules in Case of Multimedia Data Mining
... A handful of multimedia data mining approaches are available for many potential information and association from large amount of multimedia data. A brief survey of some recent researches related to mining association from multimedia data is presented here. A generalized Affinity – based association ...
... A handful of multimedia data mining approaches are available for many potential information and association from large amount of multimedia data. A brief survey of some recent researches related to mining association from multimedia data is presented here. A generalized Affinity – based association ...
Combining Models to Improve Classifier Accuracy
... therefore no means to overcome erroneous predictions. Decision trees are very unstable in this regard as small perturbations in the training data set can produce large differences in the structure (and predictions) of a model. Neural networks are sensitive to data used to train the models and to the ...
... therefore no means to overcome erroneous predictions. Decision trees are very unstable in this regard as small perturbations in the training data set can produce large differences in the structure (and predictions) of a model. Neural networks are sensitive to data used to train the models and to the ...
Data Mining PowerPoint Slide Presentation
... Don’t jump to conclusions; perform process audits as needed Don’t be a “one widget wonder”; integrate multiple paradigms so the strengths of one compensate for the weaknesses of another Break the problem into the right pieces (“Divide and Conquer”) Work the data, not the tools, but automate when pos ...
... Don’t jump to conclusions; perform process audits as needed Don’t be a “one widget wonder”; integrate multiple paradigms so the strengths of one compensate for the weaknesses of another Break the problem into the right pieces (“Divide and Conquer”) Work the data, not the tools, but automate when pos ...
doc - OoCities
... can uncover facts behind a sudden spurt in the sale of items and determine crucial issues that keep a client loyal. This may entail analyzing customer details and thereby help devise effective and better marketing strategies. One can use cluster analysis to identify customers suitable for cross-sell ...
... can uncover facts behind a sudden spurt in the sale of items and determine crucial issues that keep a client loyal. This may entail analyzing customer details and thereby help devise effective and better marketing strategies. One can use cluster analysis to identify customers suitable for cross-sell ...
Data Mining of Web Access Logs Using Classification
... transactions. It may be possible that a visitor starts a visit at 12:55 pm and ends at 00:11 am in which case there will be two transactions generated instead of one. ...
... transactions. It may be possible that a visitor starts a visit at 12:55 pm and ends at 00:11 am in which case there will be two transactions generated instead of one. ...
Full Report
... demographic information (GPA). This data is allowing us to research mobile data use patterns at ACU. We believe that this data will better inform us as to how users take their devices and learn within the educational environment. We are gathering this data in order to answer questions like “How does ...
... demographic information (GPA). This data is allowing us to research mobile data use patterns at ACU. We believe that this data will better inform us as to how users take their devices and learn within the educational environment. We are gathering this data in order to answer questions like “How does ...
use of machine learning algorithms to predict the incidence of lead
... importance of the predictor variables. The top predictor variable is given a score of 100 and then all other predictors are ranked in descending order, based on their influence upon the target variable, in comparison with the top predictor. To further aid in the interpretation of the data, graphic d ...
... importance of the predictor variables. The top predictor variable is given a score of 100 and then all other predictors are ranked in descending order, based on their influence upon the target variable, in comparison with the top predictor. To further aid in the interpretation of the data, graphic d ...
Combining Predictive Analytics with Business Rules to make better
... • Selects the appropriate actions so they are incorporated in the business processes Holds the key for • Optimized performance • Informed decisions • Actionable insight • Trusted information Help answer the questions • What is happening • Why is it happening • What is likely to happen • How should w ...
... • Selects the appropriate actions so they are incorporated in the business processes Holds the key for • Optimized performance • Informed decisions • Actionable insight • Trusted information Help answer the questions • What is happening • Why is it happening • What is likely to happen • How should w ...
**** 1 - Data Mining Lab
... A theoretical analysis for extracting only discriminative sequential patterns A technique for improving performance by limiting the length of sequential patterns without losing accuracy not covered in detail ...
... A theoretical analysis for extracting only discriminative sequential patterns A technique for improving performance by limiting the length of sequential patterns without losing accuracy not covered in detail ...
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.