Business Intelligence/ Decision Models
... XYZ wants to retain more customers by identifying likely churners 2 months prior and putting an action in place to ...
... XYZ wants to retain more customers by identifying likely churners 2 months prior and putting an action in place to ...
Mining Frequent Patterns in Data Streams at Multiple Time
... 1) Data streams arrive item by item. Each item contains attribute values for a1,a2, …,an attributes and the class category. ...
... 1) Data streams arrive item by item. Each item contains attribute values for a1,a2, …,an attributes and the class category. ...
NIM: Scalable Distributed Stream Processing System on Mobile Network Data
... recognition of NIM is identical to that of off line analysis, while NIM can process data at line rate. The design and the unique features (e.g., balanced data grouping, aging strategy) of NIM will be helpful not only for the network data analysis but also for other applications. ...
... recognition of NIM is identical to that of off line analysis, while NIM can process data at line rate. The design and the unique features (e.g., balanced data grouping, aging strategy) of NIM will be helpful not only for the network data analysis but also for other applications. ...
slides - UCLA Computer Science
... Pre-assign classes to obtain an approximate result and provide simple models/rules. Decompose the feature space to make classification decisions. Akin to wavelets. ...
... Pre-assign classes to obtain an approximate result and provide simple models/rules. Decompose the feature space to make classification decisions. Akin to wavelets. ...
Project Idea My project idea is, use and learn from the data given to
... outcome. I have been following games and news of NBA closely, so I think it would be great to do the project related to something I'm interested. There are many experts and gambling sites trying to find the best program to predict the outcome of a game, so I also think that it will be a good practic ...
... outcome. I have been following games and news of NBA closely, so I think it would be great to do the project related to something I'm interested. There are many experts and gambling sites trying to find the best program to predict the outcome of a game, so I also think that it will be a good practic ...
Final Year Project
... • Find a model for class attribute as a function of others to group a class for previously unseen records • e.g. find out the classifier for historic stock price; Group companies into different classes for inspection • classier: decision tree, rule-based classifier ...
... • Find a model for class attribute as a function of others to group a class for previously unseen records • e.g. find out the classifier for historic stock price; Group companies into different classes for inspection • classier: decision tree, rule-based classifier ...
Geographical Data Mining - wiki DPI
... – Few data manipulation techniques – Few information extraction tools ...
... – Few data manipulation techniques – Few information extraction tools ...
Mahout
... • Create scalable machine learning libraries • Why ? • Many Open Source ML libraries either: ...
... • Create scalable machine learning libraries • Why ? • Many Open Source ML libraries either: ...
PPT
... stress = (ij(D(Si,Sj) - D(Ski, Skj) )2 / ijD(Si,Sj) 2) 1/2 – where D(Si,Sj) be the distance between time series Si, Sj, and D(Ski, Skj) be the Euclidean distance of the k-dim representations • Steepest descent algorithm: – start with an assignment (time series to k-dim point) – minimize stress by ...
... stress = (ij(D(Si,Sj) - D(Ski, Skj) )2 / ijD(Si,Sj) 2) 1/2 – where D(Si,Sj) be the distance between time series Si, Sj, and D(Ski, Skj) be the Euclidean distance of the k-dim representations • Steepest descent algorithm: – start with an assignment (time series to k-dim point) – minimize stress by ...
How to enhance shareholders value through data mining technique
... have been advised to adopt a culture of decision-making using data mining technique. Also identified as knowledge discovery, data mining is the process of analysing data from different perspectives and summarising it into useful information. Data mining information, according to experts, can be used ...
... have been advised to adopt a culture of decision-making using data mining technique. Also identified as knowledge discovery, data mining is the process of analysing data from different perspectives and summarising it into useful information. Data mining information, according to experts, can be used ...
Poster
... To date, the vast majority of research on time series data mining has focused on similarity search, and to a lesser extent on clustering. We believe that these problems should now be regarded as essentially solved. In particular, there are now fast exact techniques for searching and clustering patte ...
... To date, the vast majority of research on time series data mining has focused on similarity search, and to a lesser extent on clustering. We believe that these problems should now be regarded as essentially solved. In particular, there are now fast exact techniques for searching and clustering patte ...
AVVISO DI SEMINARI Dr. Sašo Džeroski ENVIRONMENTAL
... Data mining (DM), the central activity in the process of knowledge discovery in databases (KDD), is concerned with finding patterns in data. The talk will give a crash-course introduction to data mining, followed by an overview of several applications of data mining to environmental data. Several sp ...
... Data mining (DM), the central activity in the process of knowledge discovery in databases (KDD), is concerned with finding patterns in data. The talk will give a crash-course introduction to data mining, followed by an overview of several applications of data mining to environmental data. Several sp ...
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.