college of management in trenčín using data mining as a tool for
... such a short time and contacted the company. It turned out that cheaters somehow avoided the security systems and therefore were able to see the cards of opponents; witch is in game of poker tremendous advantage. So in this case, known as Ultimate Bet scandal, data mining helped to discover fraud de ...
... such a short time and contacted the company. It turned out that cheaters somehow avoided the security systems and therefore were able to see the cards of opponents; witch is in game of poker tremendous advantage. So in this case, known as Ultimate Bet scandal, data mining helped to discover fraud de ...
ppt
... The Apriori algorithm is the most well known association rule algorithm and used in most commercial products. The name of the algorithm is based on the fact that the algorithm uses prior knowledge of frequent itemset properties. Apriori employs an iterative approach known as a level-wise search, whe ...
... The Apriori algorithm is the most well known association rule algorithm and used in most commercial products. The name of the algorithm is based on the fact that the algorithm uses prior knowledge of frequent itemset properties. Apriori employs an iterative approach known as a level-wise search, whe ...
Data Mining and Visualization of Android Usage Data
... appealing visualizations for the discovered information (use cases) from the real usage of an real Android mobile application. This dissertation exposes the implementation from scratch, adaptation and testing of an existing algorithm called Latent Dirichlet Allocation (LDA) used in Text Mining (Blei ...
... appealing visualizations for the discovered information (use cases) from the real usage of an real Android mobile application. This dissertation exposes the implementation from scratch, adaptation and testing of an existing algorithm called Latent Dirichlet Allocation (LDA) used in Text Mining (Blei ...
Title A Data Mining and Optimization-based Real
... transportation network, such as traveling time, customer orders, is continually released or updated over time during the planning period [2]. Time-varying vehicle speeds, due to dynamically changing traffic conditions, are a feature of Real-time Routing Problems where the aim is to minimize the trav ...
... transportation network, such as traveling time, customer orders, is continually released or updated over time during the planning period [2]. Time-varying vehicle speeds, due to dynamically changing traffic conditions, are a feature of Real-time Routing Problems where the aim is to minimize the trav ...
Clustering System based on Text Mining using the K
... Lemmatisation (or lemmatization) in linguistics, is the process of reducing the inflected forms or sometimes the derived forms of a word to its base form so that they can be analysed as a single term. In computational linguistic, lemmatisation is the algorithmic process of getting the normalized or ...
... Lemmatisation (or lemmatization) in linguistics, is the process of reducing the inflected forms or sometimes the derived forms of a word to its base form so that they can be analysed as a single term. In computational linguistic, lemmatisation is the algorithmic process of getting the normalized or ...
Introduction to the IEEE Transactions on Big Data
... generated and processed to meet the demands) and Veracity (the quality of the data being captured can vary greatly). These complexities pose a major challenge as well as new opportunity for today’s information technology communities. The term Big Data goes well beyond the data itself; it is also oft ...
... generated and processed to meet the demands) and Veracity (the quality of the data being captured can vary greatly). These complexities pose a major challenge as well as new opportunity for today’s information technology communities. The term Big Data goes well beyond the data itself; it is also oft ...
Tree-based Models: Identification of Influential Factors under Condition of Instability
... inputs from the original data set to be analyzed. Each tree-based model from the Pareto-optimal set is represented in this data set by one column that reflects the importance measurements of the corresponding input variables. The concept of partial lists (Dwork et al., 2000) provide a practical way ...
... inputs from the original data set to be analyzed. Each tree-based model from the Pareto-optimal set is represented in this data set by one column that reflects the importance measurements of the corresponding input variables. The concept of partial lists (Dwork et al., 2000) provide a practical way ...
Intelligent information services in environmental applications
... The quality of life, well-being and a healthy living environment, for example, are fields where new information services can assist the creation of proactive decisions to avoid environmental problems caused by industrial activity, traffic, or extraordinary weather conditions. The combination of data ...
... The quality of life, well-being and a healthy living environment, for example, are fields where new information services can assist the creation of proactive decisions to avoid environmental problems caused by industrial activity, traffic, or extraordinary weather conditions. The combination of data ...
Hierarchical Clustering - delab-auth
... x Starting with some pairs of clusters having three initial centroids, while other have only one. © Tan,Steinbach, Kumar ...
... x Starting with some pairs of clusters having three initial centroids, while other have only one. © Tan,Steinbach, Kumar ...
Download Syllabus
... on how to use data to develop insights and predictive capabilities using machine learning, data mining and forecasting techniques. In the second part, we focus on the use of optimization to support decision-making in the presence of a large number of alternatives and business constraints. Finally, t ...
... on how to use data to develop insights and predictive capabilities using machine learning, data mining and forecasting techniques. In the second part, we focus on the use of optimization to support decision-making in the presence of a large number of alternatives and business constraints. Finally, t ...
Mining Infrequent Patterns across Multiple Streams of Data
... Infrequent pattern mining is concerned with extracting “un- typical approach [10] to infrequent pattern mining is to first common” or ”scarce” patterns from streams of data. In the identify the frequent patterns and then prune these patterns past, frequent pattern mining has been investigated in det ...
... Infrequent pattern mining is concerned with extracting “un- typical approach [10] to infrequent pattern mining is to first common” or ”scarce” patterns from streams of data. In the identify the frequent patterns and then prune these patterns past, frequent pattern mining has been investigated in det ...
computational information design
... One significant difficulty with such problems is knowing, given a set of data, how to glean meaningful information from it. To most, the process is entirely opaque. Fields such as statistics, data mining, graphic design, and information visualization each offer components of the solution, but practi ...
... One significant difficulty with such problems is knowing, given a set of data, how to glean meaningful information from it. To most, the process is entirely opaque. Fields such as statistics, data mining, graphic design, and information visualization each offer components of the solution, but practi ...
Mining Health Data for Breast Cancer Diagnosis Using Machine
... cost, the waiting time, and free human experts (physicians) for more research, as well as reduce the errors and mistakes that can be made by humans due to fatigue and tiredness. However, the process of utilising health data effectively, involves many challenges such as the problem of missing feature ...
... cost, the waiting time, and free human experts (physicians) for more research, as well as reduce the errors and mistakes that can be made by humans due to fatigue and tiredness. However, the process of utilising health data effectively, involves many challenges such as the problem of missing feature ...
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... cluster (it has been merged with others before), the other is also merged into that composite cluster. When both of them have been merged, if they belong to the same composite cluster, this pair is skipped; otherwise, the two composite clusters are merged together. This process continues until there ...
... cluster (it has been merged with others before), the other is also merged into that composite cluster. When both of them have been merged, if they belong to the same composite cluster, this pair is skipped; otherwise, the two composite clusters are merged together. This process continues until there ...
powerpoint slides
... Classical method: logistic regression, decision trees, bayesian classifier assumes learning samples are independent of each other Spatial auto-correlation violates this assumption! Q? What will a map look like where the properties of a pixel was independent of the properties of other pixels? (see be ...
... Classical method: logistic regression, decision trees, bayesian classifier assumes learning samples are independent of each other Spatial auto-correlation violates this assumption! Q? What will a map look like where the properties of a pixel was independent of the properties of other pixels? (see be ...
The Survey of Data Mining Applications
... (e.g., doctor’s notes or clinical records), it is necessary to also explore the use of text mining to expand the scope and nature of what healthcare data mining can currently do. In particular, it is useful to be able to integrate data and text mining. It is also useful to look into how images (e.g. ...
... (e.g., doctor’s notes or clinical records), it is necessary to also explore the use of text mining to expand the scope and nature of what healthcare data mining can currently do. In particular, it is useful to be able to integrate data and text mining. It is also useful to look into how images (e.g. ...
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.