Meta-Learning Rule Learning Heuristics
... wide variety of rule evaluation metrics were analyzed and compared by visualizing their behavior in ROC space. There is some work on introducing new heuristics but all of them were found under the condition of a fixed bias. For example, in [7] they adjusted parameters of three heuristics, whose shap ...
... wide variety of rule evaluation metrics were analyzed and compared by visualizing their behavior in ROC space. There is some work on introducing new heuristics but all of them were found under the condition of a fixed bias. For example, in [7] they adjusted parameters of three heuristics, whose shap ...
Czech Technical University in Prague Faculty of Electrical
... The methods are based on artificial neural network and evolutionary computation. The proposed methods are designed to adapt to the problem,and thus, to provide robust and efficient algorithms and their results in comparison to other used techniques on real world problems. In the past, research in th ...
... The methods are based on artificial neural network and evolutionary computation. The proposed methods are designed to adapt to the problem,and thus, to provide robust and efficient algorithms and their results in comparison to other used techniques on real world problems. In the past, research in th ...
Spatial Data Mining Techniques
... have high similarity in comparison to one another, but are dissimilar to objects in other clusters. For example, clustering ...
... have high similarity in comparison to one another, but are dissimilar to objects in other clusters. For example, clustering ...
Applied Data Mining for Business Intelligence
... Query-Reporting-Analysis - This type of analysis is often query based and is normally used for determining ”What happened?” in a business over a given period of time. Because queries are used the user already knows what kind of information to search for. Additionally, BI solutions of this kind are g ...
... Query-Reporting-Analysis - This type of analysis is often query based and is normally used for determining ”What happened?” in a business over a given period of time. Because queries are used the user already knows what kind of information to search for. Additionally, BI solutions of this kind are g ...
Automated Discovery of Novel Anomalous Patterns
... In this section, we review the related work, which can be categorized into three major groups: intrusion detection, rare category analysis and anomalous pattern detection for general data. Additionally, we juxtapose the general tasks of anomalous pattern discovery and anomalous pattern detection, wh ...
... In this section, we review the related work, which can be categorized into three major groups: intrusion detection, rare category analysis and anomalous pattern detection for general data. Additionally, we juxtapose the general tasks of anomalous pattern discovery and anomalous pattern detection, wh ...
Efficient Mining of Association Rules Based on Formal Concept
... B2 are frequent concept intents and where the concept (B10 , B1 ) is an immediate subconcept of (B20 , B2 ). Hence there corresponds to each approximate rule in the Luxenburger base exactly one edge in the line diagram. Figure 5 visualizes all rules in the Luxenburger basis for minsupp = 70 % and mi ...
... B2 are frequent concept intents and where the concept (B10 , B1 ) is an immediate subconcept of (B20 , B2 ). Hence there corresponds to each approximate rule in the Luxenburger base exactly one edge in the line diagram. Figure 5 visualizes all rules in the Luxenburger basis for minsupp = 70 % and mi ...
Course Resources
... – Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies ...
... – Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies ...
Subspace Clustering for Complex Data
... objects, i.e. all characteristics of the objects are taken into account. While collecting more and more characteristics, however, it is very unlikely that two objects are similar with respect to the full space and often some dimensions are not relevant for clustering. A continuative aspect is the de ...
... objects, i.e. all characteristics of the objects are taken into account. While collecting more and more characteristics, however, it is very unlikely that two objects are similar with respect to the full space and often some dimensions are not relevant for clustering. A continuative aspect is the de ...
as a PDF
... B2 are frequent concept intents and where the concept (B10 , B1 ) is an immediate subconcept of (B20 , B2 ). Hence there corresponds to each approximate rule in the Luxenburger base exactly one edge in the line diagram. Figure 5 visualizes all rules in the Luxenburger basis for minsupp = 70 % and mi ...
... B2 are frequent concept intents and where the concept (B10 , B1 ) is an immediate subconcept of (B20 , B2 ). Hence there corresponds to each approximate rule in the Luxenburger base exactly one edge in the line diagram. Figure 5 visualizes all rules in the Luxenburger basis for minsupp = 70 % and mi ...
User Intention Modeling in Web Applications Using Data Mining
... that the user typed for precision improvement using a so-called query domain analysis approach. Other information agents in this kind, such as WebWatcher [1], WebMate [5], WAIR [17] are based on a similar approach. For instance, in WebWatcher, hyperlink information is considered, and in WAIR, user’s ...
... that the user typed for precision improvement using a so-called query domain analysis approach. Other information agents in this kind, such as WebWatcher [1], WebMate [5], WAIR [17] are based on a similar approach. For instance, in WebWatcher, hyperlink information is considered, and in WAIR, user’s ...
Multidimensional Sequential Pattern Mining
... diagnosis from the sequence of symptoms experienced; over customer data to help target repeat customers; and with web-log data to better structure a company’s website for easy accessibility of most popular links. There are several known methods for discovering general sequential patterns at present. ...
... diagnosis from the sequence of symptoms experienced; over customer data to help target repeat customers; and with web-log data to better structure a company’s website for easy accessibility of most popular links. There are several known methods for discovering general sequential patterns at present. ...
ppt
... Data Discrimination Comparing the target class with one or a set of comparative classes (contrasting classes) ...
... Data Discrimination Comparing the target class with one or a set of comparative classes (contrasting classes) ...
ICS 278: Data Mining Lecture 1: Introduction to Data Mining
... • There are heuristics to try to infer the true actions of the user: – Path completion (Cooley et al. 1999) • e.g. If known B -> F and not C -> F, then session ABCF can be interpreted as ABCBF • Anderson et al. 2001 for more heuristics ...
... • There are heuristics to try to infer the true actions of the user: – Path completion (Cooley et al. 1999) • e.g. If known B -> F and not C -> F, then session ABCF can be interpreted as ABCBF • Anderson et al. 2001 for more heuristics ...
Outlier Detection for Temporal Data: A Survey
... to generate the state transition rules depend on particular application domains. Some Markov methods store conditional information for a fixed history size=k, while others use a variable history size to capture richer temporal dependencies. Ye [38] proposes a technique where a Markov model with k=1 ...
... to generate the state transition rules depend on particular application domains. Some Markov methods store conditional information for a fixed history size=k, while others use a variable history size to capture richer temporal dependencies. Ye [38] proposes a technique where a Markov model with k=1 ...
Comparing Expert and Metric-Based Assessments of Association
... (e.g. Romero & Ventura, 2007; Baker & Yacef, 2009; Scheuer & McLaren, 2012; Baker & Siemens, in press). Sequential Pattern Mining consists of finding association rules where the contents of the then-clause occur temporally after the contents of the if-clause (Agrawal & Srikant, 1995). In the case of ...
... (e.g. Romero & Ventura, 2007; Baker & Yacef, 2009; Scheuer & McLaren, 2012; Baker & Siemens, in press). Sequential Pattern Mining consists of finding association rules where the contents of the then-clause occur temporally after the contents of the if-clause (Agrawal & Srikant, 1995). In the case of ...
Master`s Thesis: Mining for Frequent Events in Time Series
... has been done on mining numeric time series data. This stems primarily from the problems of relating numeric data, which likely contains error or other variations which make directly relating values difficult. To handle this problem, many algorithms first convert data into a sequence of events. In s ...
... has been done on mining numeric time series data. This stems primarily from the problems of relating numeric data, which likely contains error or other variations which make directly relating values difficult. To handle this problem, many algorithms first convert data into a sequence of events. In s ...
Data Mining Association Rules: Advanced Concepts and Algorithms
... Min-Apriori (Han et al) Document-term matrix: ...
... Min-Apriori (Han et al) Document-term matrix: ...
Online outlier detection over data streams
... different from the remainder as if it is generated by a different mechanism. Outlier detection is an important data mining research direction with numerous applications, including credit card fraud detection, discovery of criminal activities in electronic commerce, weather prediction, marketing and ...
... different from the remainder as if it is generated by a different mechanism. Outlier detection is an important data mining research direction with numerous applications, including credit card fraud detection, discovery of criminal activities in electronic commerce, weather prediction, marketing and ...
Continuous Trend-Based Classification of Streaming Time Series
... – We must delete (if necessary) the corresponding stream from the old class and assign it to a new one, and – We must report efficiently the stream identifiers that belong to a specific trend class. Each trend class is supported by several lists of buckets. The first bucket of each list is the prim ...
... – We must delete (if necessary) the corresponding stream from the old class and assign it to a new one, and – We must report efficiently the stream identifiers that belong to a specific trend class. Each trend class is supported by several lists of buckets. The first bucket of each list is the prim ...
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.