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computational methods for learning and inference on dynamic
... complex physical, biological, and social phenomena ranging from protein-protein interactions to the formation of social acquaintances can be naturally represented by networks. Much effort has been dedicated to analyzing real-world networks to reveal their often complex structure. Empirical findings ...
... complex physical, biological, and social phenomena ranging from protein-protein interactions to the formation of social acquaintances can be naturally represented by networks. Much effort has been dedicated to analyzing real-world networks to reveal their often complex structure. Empirical findings ...
K-means Clustering Versus Validation Measures: A Data
... a well-known and widely used partitional clustering method, K-means [30] has attracted great interest in the literature. There are considerable research efforts to characterize the key features of the K-means clustering algorithms. Indeed, people have identified some data characteristics that may st ...
... a well-known and widely used partitional clustering method, K-means [30] has attracted great interest in the literature. There are considerable research efforts to characterize the key features of the K-means clustering algorithms. Indeed, people have identified some data characteristics that may st ...
TESI DOCTORAL
... making use of rule lists to choose the best action to a given problem situation, acquiring their knowledge through the experience. LCSs have been applied with relative success to a wide set of real-world problems such as cancer prediction or business support systems, among many others. Furthermore, ...
... making use of rule lists to choose the best action to a given problem situation, acquiring their knowledge through the experience. LCSs have been applied with relative success to a wide set of real-world problems such as cancer prediction or business support systems, among many others. Furthermore, ...
Iterative Root Cause Analysis Using Data Mining in
... tool Splunk in this thesis as an example; however, the practices presented in this research can be applied to other similar tools. We conduct root cause analysis by mining system logs generated by mobile base stations, to investigate which system component is causing the base station to fall short o ...
... tool Splunk in this thesis as an example; however, the practices presented in this research can be applied to other similar tools. We conduct root cause analysis by mining system logs generated by mobile base stations, to investigate which system component is causing the base station to fall short o ...
Pattern Management - Biblioteca Central UABCS
... Since in the integrated architecture a unique data model is used for both data and patterns, design of the pattern base is simplified. For example, an association rule can be represented in the relational model by using a set of relational tuples, each containing the head of the rule and one element ...
... Since in the integrated architecture a unique data model is used for both data and patterns, design of the pattern base is simplified. For example, an association rule can be represented in the relational model by using a set of relational tuples, each containing the head of the rule and one element ...
List of Abbreviations
... Intelligence mechanisms so that it is not known how often a particular rule or prediction function was used or which results it produced with what frequency. In this master thesis, prerequisites and requirements for measuring success of Customer Intelligence mechanisms will be investigated. Research ...
... Intelligence mechanisms so that it is not known how often a particular rule or prediction function was used or which results it produced with what frequency. In this master thesis, prerequisites and requirements for measuring success of Customer Intelligence mechanisms will be investigated. Research ...
PRIVACY-PRESERVING AND DATA UTILITY IN GRAPH MINING
... to third parties. In this scenario, anonymization processes become an important concern. However, anonymization processes usually introduce some kind of noise in the anonymous data, altering the data and also their results on graph mining processes. Generally, the higher the privacy, the larger the ...
... to third parties. In this scenario, anonymization processes become an important concern. However, anonymization processes usually introduce some kind of noise in the anonymous data, altering the data and also their results on graph mining processes. Generally, the higher the privacy, the larger the ...
PRIVACY-PRESERVING AND DATA UTILITY IN GRAPH MINING
... to third parties. In this scenario, anonymization processes become an important concern. However, anonymization processes usually introduce some kind of noise in the anonymous data, altering the data and also their results on graph mining processes. Generally, the higher the privacy, the larger the ...
... to third parties. In this scenario, anonymization processes become an important concern. However, anonymization processes usually introduce some kind of noise in the anonymous data, altering the data and also their results on graph mining processes. Generally, the higher the privacy, the larger the ...
Mining Emerging Gradual Patterns
... data, described by real values associated to numerical features. GP are linguistically expressed in the form “the more A increases, the more B increases”, or equivalently, “the higher A, the higher B”. They impose constraints across the whole data set and ...
... data, described by real values associated to numerical features. GP are linguistically expressed in the form “the more A increases, the more B increases”, or equivalently, “the higher A, the higher B”. They impose constraints across the whole data set and ...
An Efficient Sliding Window Based Algorithm for
... capacities of computing systems. Sliding window is an interesting model to solve this problem since it does not need the entire history of received transactions and can handle concept change by considering only a limited range of recent transactions. However, previous sliding window algorithms requi ...
... capacities of computing systems. Sliding window is an interesting model to solve this problem since it does not need the entire history of received transactions and can handle concept change by considering only a limited range of recent transactions. However, previous sliding window algorithms requi ...
A GPS server with road mapping and data mining
... Traffic control and management currently requires high-tech computerized solutions as opposed to the manual methods that commonly involve the use of traffic policemen, traffic lights and safety cameras. Collection and analysis of road traffic data is a key requirement towards establishment of traffi ...
... Traffic control and management currently requires high-tech computerized solutions as opposed to the manual methods that commonly involve the use of traffic policemen, traffic lights and safety cameras. Collection and analysis of road traffic data is a key requirement towards establishment of traffi ...
... database, that is, the number of past baskets ordered by the online grocery store customer. Most of our bounds are derived using a specific notion of algorithmic stability called “pointwise hypothesis stability.” The original notions of algorithmic stability were invented in the 1970’s and have been ...
Analysis of Twitter Data Using a Multiple
... We evaluated the usefulness and applicability of the proposed approach on two real datasets retrieved from Twitter (http://twitter.com). Our framework exploits a crawler to access the Twitter global stream efficiently. To generate the real Twitter datasets we monitored the public stream endpoint off ...
... We evaluated the usefulness and applicability of the proposed approach on two real datasets retrieved from Twitter (http://twitter.com). Our framework exploits a crawler to access the Twitter global stream efficiently. To generate the real Twitter datasets we monitored the public stream endpoint off ...
When Pattern met Subspace Cluster
... pattern mining, we adopt a visual approach; if we are allowed to re-order both attributes and objects freely, we can reorder D and A such that C and A dene a rectangle in the data, or a tile. In pattern mining, the notion of a tile has become very important in recent years [17, 21, 23, 33]. Origin ...
... pattern mining, we adopt a visual approach; if we are allowed to re-order both attributes and objects freely, we can reorder D and A such that C and A dene a rectangle in the data, or a tile. In pattern mining, the notion of a tile has become very important in recent years [17, 21, 23, 33]. Origin ...
Using Constraints During Set Mining: Should We Prune or not?
... Therefore, we need query languages that enable the user to select subsets of data as well as tightly specified theories. This gave rise to the concept of inductive databases, i.e., databases that contain intensionally defined theories in addition to the usual data. This framework has been suggested ...
... Therefore, we need query languages that enable the user to select subsets of data as well as tightly specified theories. This gave rise to the concept of inductive databases, i.e., databases that contain intensionally defined theories in addition to the usual data. This framework has been suggested ...
ke.informatik.tu-darmstadt.de
... a handful of patterns is clearly infeasible. Furthermore, when inducing global models from the set of local patterns, machine learning procedures tend to be hindered by the presence of many, often redundant, features. The goal of the Pattern Set Discovery phase therefore, is to reduce the redundancy ...
... a handful of patterns is clearly infeasible. Furthermore, when inducing global models from the set of local patterns, machine learning procedures tend to be hindered by the presence of many, often redundant, features. The goal of the Pattern Set Discovery phase therefore, is to reduce the redundancy ...
Involving Aggregate Functions in Multi-Relational Search
... some disadvantages. These are due to the fact that all multi-relational features are constructed statically during a preprocessing stage, before the actual searching is done. In contrast, most MRDM algorithms select a new set of relevant features dynamically, based on a subset of examples under inve ...
... some disadvantages. These are due to the fact that all multi-relational features are constructed statically during a preprocessing stage, before the actual searching is done. In contrast, most MRDM algorithms select a new set of relevant features dynamically, based on a subset of examples under inve ...
Intelligent knowledge discovery on building energy and indoor
... This work was done at the Research Group of Environmental Informatics at the University of Eastern Finland during the years 2012-2016. The work was mainly carried out in the framework of the INSULAVO (Improving Energy Efficiency of Buildings in Eastern Finland), AsKo (The Effects of Repair and Compl ...
... This work was done at the Research Group of Environmental Informatics at the University of Eastern Finland during the years 2012-2016. The work was mainly carried out in the framework of the INSULAVO (Improving Energy Efficiency of Buildings in Eastern Finland), AsKo (The Effects of Repair and Compl ...
Nonlinear dimensionality reduction
![](https://commons.wikimedia.org/wiki/Special:FilePath/Lle_hlle_swissroll.png?width=300)
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.