Full Text - MECS Publisher
... also uses another set called the Maximum Frequent Set (MFS) which contains all the maximal frequent itemsets identified during the process. Any itemset that is classified as infrequent in bottom-up approach is used to update MFCS. Any itemset that is classified as frequent in the top-down approach i ...
... also uses another set called the Maximum Frequent Set (MFS) which contains all the maximal frequent itemsets identified during the process. Any itemset that is classified as infrequent in bottom-up approach is used to update MFCS. Any itemset that is classified as frequent in the top-down approach i ...
SMM: a Data Stream Management System for Knowledge Discovery
... Weka, with the performance and QoS guarantees of a DSMS. This is accomplished in three main steps. The first is an open and extensible DSMS architecture where KDD queries can be easily expressed as user-defined aggregates (UDAs)—our system combines that with the efficiency of synoptic data structure ...
... Weka, with the performance and QoS guarantees of a DSMS. This is accomplished in three main steps. The first is an open and extensible DSMS architecture where KDD queries can be easily expressed as user-defined aggregates (UDAs)—our system combines that with the efficiency of synoptic data structure ...
Knowledge Discovery from Data Streams
... A transaction database and all possible frequent itemsets. . . The search space to find all possible frequent itemsets. . . . . ...
... A transaction database and all possible frequent itemsets. . . The search space to find all possible frequent itemsets. . . . . ...
The SIGSPATIAL Special
... With the rapid improvement of GPS-based tracking technology – receivers getting much smaller and batteries lasting much longer – a sudden overabundance of movement data triggered a gold-rush like enthusiasm amongst theory and application researchers. This shift from a data poor to a data rich proble ...
... With the rapid improvement of GPS-based tracking technology – receivers getting much smaller and batteries lasting much longer – a sudden overabundance of movement data triggered a gold-rush like enthusiasm amongst theory and application researchers. This shift from a data poor to a data rich proble ...
as a PDF
... 11. Distributed Algorithms, - Lynch et al., Atomic Transactions, 12. Casevant & Singhal, Readings in Distributed Computing Systems, 13. Ananda & Srinivasan, Distributed Computing Systems: Concepts and Structures Mullender, Distributed ...
... 11. Distributed Algorithms, - Lynch et al., Atomic Transactions, 12. Casevant & Singhal, Readings in Distributed Computing Systems, 13. Ananda & Srinivasan, Distributed Computing Systems: Concepts and Structures Mullender, Distributed ...
k+1
... – A frequent (k-1)-sequence w1 is merged with another frequent (k-1)-sequence w2 to produce a candidate k-sequence if the subsequence obtained by removing the first event in w1 is the same as the subsequence obtained by removing the last event in w2 The resulting candidate after merging is given by ...
... – A frequent (k-1)-sequence w1 is merged with another frequent (k-1)-sequence w2 to produce a candidate k-sequence if the subsequence obtained by removing the first event in w1 is the same as the subsequence obtained by removing the last event in w2 The resulting candidate after merging is given by ...
Incremental Affinity Propagation Clustering Based on Message
... we extend a recently proposed clustering algorithm, affinity propagation (AP) clustering, to handle dynamic data. Several experiments have shown its consistent superiority over the previous algorithms in static data. AP clustering is an exemplar-based method that realized by assigning each data poin ...
... we extend a recently proposed clustering algorithm, affinity propagation (AP) clustering, to handle dynamic data. Several experiments have shown its consistent superiority over the previous algorithms in static data. AP clustering is an exemplar-based method that realized by assigning each data poin ...
Ensemble of Feature Selection Techniques for High
... This section provides a brief coverage of the works performed in the area of ensemble feature ranking. These works assess how an ensemble of feature ranking techniques can improve robustness, performance and diversity. Feature ranking is a process of selecting the most relevant features from a large ...
... This section provides a brief coverage of the works performed in the area of ensemble feature ranking. These works assess how an ensemble of feature ranking techniques can improve robustness, performance and diversity. Feature ranking is a process of selecting the most relevant features from a large ...
Proceedings of the ACM SIGKDD Workshop on Interactive Data
... is very large; this section focuses on related work on tools for automated suggestions for portions of the exploratory data analysis process. Many intuitive user interface features that would be ideal to have for an exploratory data analysis tool are available in Tableau [1] which is descended from ...
... is very large; this section focuses on related work on tools for automated suggestions for portions of the exploratory data analysis process. Many intuitive user interface features that would be ideal to have for an exploratory data analysis tool are available in Tableau [1] which is descended from ...
Improving and maintaining prediction accuracy in agent based
... change in the environment over time. Thus, an ABM not learning regularly from its environment cannot sustain its validity over a longer period of time. This thesis describes a novel approach for incorporating adaptability and learning in an ABM simulation, in order to improve and maintain its predic ...
... change in the environment over time. Thus, an ABM not learning regularly from its environment cannot sustain its validity over a longer period of time. This thesis describes a novel approach for incorporating adaptability and learning in an ABM simulation, in order to improve and maintain its predic ...
Printable version - ugweb.cs.ualberta.ca
... • Assume that there are two classes, P and N. – Let the set of examples S contain x elements of class P and y elements of class N. – The amount of information, needed to decide if an arbitrary example in S belong to P or N is defined as: ...
... • Assume that there are two classes, P and N. – Let the set of examples S contain x elements of class P and y elements of class N. – The amount of information, needed to decide if an arbitrary example in S belong to P or N is defined as: ...
Information Mining Technologies to Enable Discovery of Actionable
... The current focus of the data mining community is the application of data mining to nonstandard data sets (i.e. non-tabular data sets) such as image sets, documents, video, multimedia data, network data, matrices, graphs and tensors. For the last three listed data sets, the data mining algorithms em ...
... The current focus of the data mining community is the application of data mining to nonstandard data sets (i.e. non-tabular data sets) such as image sets, documents, video, multimedia data, network data, matrices, graphs and tensors. For the last three listed data sets, the data mining algorithms em ...
Constraint Programming meets Machine Learning and Data Mining
... constraints that underly their application. This is often a difficult task. Even when the right constraints are known, it can be challenging to formalize them in such a way that the constraint programming system can use them efficiently. This raises the question as to whether it is possible to (semi ...
... constraints that underly their application. This is often a difficult task. Even when the right constraints are known, it can be challenging to formalize them in such a way that the constraint programming system can use them efficiently. This raises the question as to whether it is possible to (semi ...
Representing Entities in the OntoDM Data Mining Ontology
... various biological and technological domains and domain specific terms relevant only to a given domain. The ontology supports consistent annotation of biomedical investigations regardless of the particular field of the study [6]. OBI defines an investigation as a process with several parts, including p ...
... various biological and technological domains and domain specific terms relevant only to a given domain. The ontology supports consistent annotation of biomedical investigations regardless of the particular field of the study [6]. OBI defines an investigation as a process with several parts, including p ...
Parallel Itemset Mining in Massively Distributed Environments
... Since few decades ago, the volume of data has been increasingly growing. The rapid advances that have been made in computer storage have offered a great flexibility in storing very large amounts of data. The processing of these massive volumes of data have opened up new challenges in data mining. In ...
... Since few decades ago, the volume of data has been increasingly growing. The rapid advances that have been made in computer storage have offered a great flexibility in storing very large amounts of data. The processing of these massive volumes of data have opened up new challenges in data mining. In ...
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.