Mining Frequent Spatio-Temporal Patterns from
... patterns inside a geographical area. These data are available from different sources, like GPS traces extracted from these devices or from internet sites where users voluntarily share their location among other information. Different knowledge can be extracted from these data depending on the analys ...
... patterns inside a geographical area. These data are available from different sources, like GPS traces extracted from these devices or from internet sites where users voluntarily share their location among other information. Different knowledge can be extracted from these data depending on the analys ...
Discovery of Spatio-Temporal Patterns from Location
... Different geographical discretizations can be proposed to allow the extraction of patterns at different resolutions. A possibility is to divide the area using a regular grid. This would also allow to study the events at different levels of granularity. Controlling the size and shape of the cells it ...
... Different geographical discretizations can be proposed to allow the extraction of patterns at different resolutions. A possibility is to divide the area using a regular grid. This would also allow to study the events at different levels of granularity. Controlling the size and shape of the cells it ...
A Clustering-based Approach for Discovering Interesting Places in
... over trajectories, which allows powerful semantic analysis, called stops and moves. A stop is a semantically important part of a trajectory that is relevant for an application, and where the object has stayed for a minimal amount of time. For instance, in a tourism application, a stop could be a tou ...
... over trajectories, which allows powerful semantic analysis, called stops and moves. A stop is a semantically important part of a trajectory that is relevant for an application, and where the object has stayed for a minimal amount of time. For instance, in a tourism application, a stop could be a tou ...
Approximation Algorithms for Clustering Uncertain Data
... Clustering data is the topic of much study, and has generated many books devoted solely to the subject. Within database research, various methods have proven popular, such as DBSCAN [13], CURE [16], and BIRCH [28]. Algorithms have been proposed for a variety of clustering problems, such as the k-mea ...
... Clustering data is the topic of much study, and has generated many books devoted solely to the subject. Within database research, various methods have proven popular, such as DBSCAN [13], CURE [16], and BIRCH [28]. Algorithms have been proposed for a variety of clustering problems, such as the k-mea ...
Data Mining for the Internet of Things: Literature
... Bayesian networks are directed acyclic graphs whose nodes represent random variables in the Bayesian sense. Edges represent conditional dependencies; nodes which are not connected represent variables which are conditionally independent of each other. Based on Bayesian networks, these classifiers hav ...
... Bayesian networks are directed acyclic graphs whose nodes represent random variables in the Bayesian sense. Edges represent conditional dependencies; nodes which are not connected represent variables which are conditionally independent of each other. Based on Bayesian networks, these classifiers hav ...
A Survey Paper on Cross-Domain Sentiment Analysis
... According to this definition, given a review d, a base entry ui will have a high ranking score if there are many words wj in the review d that are also listed as neighbors for the base entry ui in the sentiment-sensitive thesaurus. To expand a vector, d, for a review d, they first rank the base entr ...
... According to this definition, given a review d, a base entry ui will have a high ranking score if there are many words wj in the review d that are also listed as neighbors for the base entry ui in the sentiment-sensitive thesaurus. To expand a vector, d, for a review d, they first rank the base entr ...
Pattern Research in the Digital Humanities: How Data Mining
... To overcome these issues, applying data mining techniques for identifying similarities, relations, and rules in the captured costume data is promising2 . However, data mining, in fact, is hardly an easy exercise to accomplish and comprehensive conceptual as well as technical knowledge about database ...
... To overcome these issues, applying data mining techniques for identifying similarities, relations, and rules in the captured costume data is promising2 . However, data mining, in fact, is hardly an easy exercise to accomplish and comprehensive conceptual as well as technical knowledge about database ...
Pattern Research in the Digital Humanities: How Data Mining
... To overcome these issues, applying data mining techniques for identifying similarities, relations, and rules in the captured costume data is promising2 . However, data mining, in fact, is hardly an easy exercise to accomplish and comprehensive conceptual as well as technical knowledge about database ...
... To overcome these issues, applying data mining techniques for identifying similarities, relations, and rules in the captured costume data is promising2 . However, data mining, in fact, is hardly an easy exercise to accomplish and comprehensive conceptual as well as technical knowledge about database ...
Experience Management with Task-Configurations - CEUR
... best-practices for specific situations and can then be applied as templates. In the context of experience management [2–4] and case-based reasoning [5], cases contain specific knowledge of previously experienced, concrete problem situations [6]. A case consists of a problem characterization, and add ...
... best-practices for specific situations and can then be applied as templates. In the context of experience management [2–4] and case-based reasoning [5], cases contain specific knowledge of previously experienced, concrete problem situations [6]. A case consists of a problem characterization, and add ...
Study on Feature Selection Methods for Text Mining
... challenge for effective text categorization. Each document in a document corpus contains much irrelevant and noisy information which eventually reduces the efficiency of text categorization. Most text categorization techniques reduce this large number of features by eliminating stopwords, or stemmin ...
... challenge for effective text categorization. Each document in a document corpus contains much irrelevant and noisy information which eventually reduces the efficiency of text categorization. Most text categorization techniques reduce this large number of features by eliminating stopwords, or stemmin ...
What is Data Mining
... During the 1990s, data mining changed from being an interesting new technology to becoming part of standard business practice. This occurred because the cost of computer disk storage went down, processing power went up, and the benefits of data mining became more apparent. Businesses began using “da ...
... During the 1990s, data mining changed from being an interesting new technology to becoming part of standard business practice. This occurred because the cost of computer disk storage went down, processing power went up, and the benefits of data mining became more apparent. Businesses began using “da ...
SELECT poet, SUM
... One of the more important functions of a data warehouse in a company that has disparate computing systems is to provide a view for management as though the company were in fact integrated. ...
... One of the more important functions of a data warehouse in a company that has disparate computing systems is to provide a view for management as though the company were in fact integrated. ...
DMForex: A Data Mining Application to Predict Currency Exchange
... industry accepted CRISP-DM guidelines was implemented. One of the modules would ...
... industry accepted CRISP-DM guidelines was implemented. One of the modules would ...
Scaling up classification rule induction through parallel processing
... window is used to induce a classifier. The induced classifier is then applied to the remaining instances. Instances that are misclassified are added to the window. The user can also specify the maximum number of instances to add to the window. Again a classifier is induced using the new window and t ...
... window is used to induce a classifier. The induced classifier is then applied to the remaining instances. Instances that are misclassified are added to the window. The user can also specify the maximum number of instances to add to the window. Again a classifier is induced using the new window and t ...
PPT - hkust cse
... One of the more important functions of a data warehouse in a company that has disparate computing systems is to provide a view for management as though the company were in fact integrated. ...
... One of the more important functions of a data warehouse in a company that has disparate computing systems is to provide a view for management as though the company were in fact integrated. ...
Visualizing Outliers - UIC Computer Science
... moderate-size datasets with a few singleton outliers. Most clustering algorithms do not scale well to larger datasets, however. A related approach, called Local Outlier Factor (LOF) [8], is similar to density-based clustering. Like DBSCAN clustering [17], it is highly sensitive to the choice of inpu ...
... moderate-size datasets with a few singleton outliers. Most clustering algorithms do not scale well to larger datasets, however. A related approach, called Local Outlier Factor (LOF) [8], is similar to density-based clustering. Like DBSCAN clustering [17], it is highly sensitive to the choice of inpu ...
Assisting Higher Education in Assessing, Predicting, and Managing
... order to capture student needs more effectively, the transformation process that converts student verbatim constructs, which are often tacit and subjective, into an explicit and objective statement of student needs, has to be emphasized. Poor understanding of student requirements and inaccurate assu ...
... order to capture student needs more effectively, the transformation process that converts student verbatim constructs, which are often tacit and subjective, into an explicit and objective statement of student needs, has to be emphasized. Poor understanding of student requirements and inaccurate assu ...
this PDF file - Southeast Europe Journal of Soft Computing
... procedures, they presented a time efficient algorithm to discover frequent itemsets [17]. Wang et al. improved the efficiency of data mining in large transaction database by applying Fast-Apriori algorithm. According to the authors, data mining engine could be derived using an integration of various ...
... procedures, they presented a time efficient algorithm to discover frequent itemsets [17]. Wang et al. improved the efficiency of data mining in large transaction database by applying Fast-Apriori algorithm. According to the authors, data mining engine could be derived using an integration of various ...
4 - Read
... implementation The quality of a clustering method is also measured by its ability to discover some or all of ...
... implementation The quality of a clustering method is also measured by its ability to discover some or all of ...
Automated Detection of Outliers in Real
... The statistical definition of an “outlier” depends on the underlying distribution of the variable in question. Thus, Mendenhall et al. [9] apply the term “outliers” to values “that lie very far from the middle of the distribution in either direction”. This intuitive definition is certainly limited t ...
... The statistical definition of an “outlier” depends on the underlying distribution of the variable in question. Thus, Mendenhall et al. [9] apply the term “outliers” to values “that lie very far from the middle of the distribution in either direction”. This intuitive definition is certainly limited t ...
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.