Global ETD Search
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Showing 1 to 14 of 14 for “"frequent pattern mining"”.
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New Approaches to Frequent and Incremental Frequent Pattern Mining
<p>Data Mining (DM) is a process for extracting interesting patterns from large volumes of data. It is one of the crucial steps in Knowledge Discovery in Databases (KDD). It involves various data mining methods that mainly fall into predictive and descriptive models. Descriptive models look for …
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FP-tree Based Spatial Co-location Pattern Mining
A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test …
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Automatic Software Performance Optimization on Modern Architectures
Frequent pattern mining is a fundamental problem in data mining and a large number of distinct algorithms have been proposed to solve it efficiently. However, no single algorithm outperforms all the others since their relative performance highly depends on the characteristics of the input data. In …
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Integrating Network Analysis and Data Mining Techniques into Effective Framework for Web Mining and Recommendation. A Framework for Web Mining and Recommendation
… We concentrate on Web usage (i.e., log) mining and Web structure mining. Analysing Web log data will reveal valuable feedback reflecting how effective the current structure of a web site is and to help the owner of a web site in understanding the behaviour of the web site visitors. We …
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GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY
The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number …
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A Study Of Data Informatics: Data Analysis And Knowledge Discovery Via A Novel Data Mining Algorithm
Frequent pattern mining (fpm) has become extremely popular among data mining researchers because it provides interesting and valuable patterns from large datasets. The decreasing cost of storage devices and the increasing availability of processing power make it possible for researchers to build …
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Strategy and methodology for enterprise data warehouse development. Integrating data mining and social networking techniques for identifying different communities within the data warehouse.
… the issues and challenges. Association rules mining and social networks have been adopted in this thesis to address the above mentioned issues and challenges. We describe an approach that uses frequent pattern mining and social network techniques to discover different communities within the …
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Semantic pattern discovery in open information extraction
… In this paper, we propose a novel semantic pattern-discovery for OpenIE (SemPatIE) framework which extracts relations in the form of typed textual pattern structures, called meta patterns and groups semantically similar pattern structures. To perform these tasks, the framework uses three …
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A data mining approach to ontology learning for automatic content-related question-answering in MOOCs.
… documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to …
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Developing Event Identification Methods for Structured and Unstructured Data Streams
… social net-work streams. We develop and extend a Frequent Pattern Mining method by proposinga dynamic support definition method to replace the fixed support. As the number oftext posts streamed each day is not fixed, a dynamic support, can adapt to the natureof data streams and can improve the …
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A framework for dynamic heterogeneous information networks change discovery based on knowledge engineering and data mining methods
… networks have been widely studied in data mining but recently, there has been renewed interest in dynamic heterogeneous information networks (DHIN) analysis because the rich temporal, structural and semantic information is hidden in this kind of network. The heterogeneity and dynamicity of …
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Summarizing data with representative patterns
… years, which poses new challenges to the data mining area. For example, uncertain data mining emerges due to its capability to model the inherent veracity of data; spatial data mining attracts much research attention as the widespread of location-based services and wearable devices. As a …
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Datenzentrierte Bestimmung von Assoziationsregeln in parallelen Datenbankarchitekturen
Die folgende Arbeit befasst sich mit der Alltagstauglichkeit moderner Massendatenverarbeitung, insbesondere mit dem Problem der Assoziationsregelanalyse. Vorhandene Datenmengen wachsen stark an, aber deren Auswertung ist für ungeübte Anwender schwierig. Daher verzichten Unternehmen auf …
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Deep Learning Based Methods for Automatic Extraction of Syntactic Patterns and their Application for Knowledge Discovery
… the relevance of syntactic dependency patterns (SDPs). Thankfully, semantic relationships exhibit adherence to distinct SDPs when connecting pairs of entities. Recognizing this fact underscores the critical importance of extracting these SDPs, particularly for specific semantic …