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Showing 1 to 20 of 46 for “"pattern mining"”.
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Sequential pattern mining with uncertain data
… of uncertain data. However, traditional data mining algorithms are usually inapplicable in uncertain data because of its probabilistic nature. Uncertainty has to be carefully handled; otherwise, it might significantly downgrade the quality of underlying data mining applications.</p> …
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Large-Scale Constraint-Based Pattern Mining
We studied the problem of constraint-based pattern mining for three different data formats, item-set, sequence and graph, and focused on mining patterns of large sizes. Colossal patterns in each data formats are studied to discover pruning properties that are useful for direct mining of these …
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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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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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Designing a Domain-Specific Accelerator for Graph Pattern Mining
Graph pattern mining (GPM) is used in a variety of domains such as bioinformatics, e-commerce and social sciences. GPM is a computationally intensive problem with an enormous amount of coarse-grain parallelism and therefore, attractive for hardware acceleration. Unfortunately, existing GPM …
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Causal Pattern Mining in Highly Heterogeneous and Temporal EHRs Data
… current state of U.S. healthcare system. Data mining techniques in conjunction with EHRs can be used to develop novel clinical decision making tools, to analyze the prevalence and incidence of diseases and to evaluate the efficacy of existing clinical and surgical interventions. In this thesis …
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Accelerating graph pattern mining algorithms on modern graphics processing units
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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DeepParse: Hybrid LLM-Guided Pattern Mining for Accurate and Reproducible Log Parsing
… that automatically mines reusable variable patterns from small log samples and applies them deterministically through the Drain algorithm. By separating the LLMs reasoning phase from runtime parsing, DeepParse enables scalable, consistent, and cost-efficient log structuring without …
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Multivariate sequential contrast pattern mining and prediction models for critical care clinical informatics
Data mining and knowledge discovery involves efficient search and discovery of patterns in data that are able to describe the underlying complex structure and properties of the corresponding system. To be of practical use, the discovered patterns need to be novel, informative and interpretable. …
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Urban computing using call detail records : mobility pattern mining, next-location prediction and location recommendation
… for billing purposes, to understand presence patterns, develop mobility prediction methods and reduce traffic congestions with location recommendations. Understanding human mobility and presence patterns at locations are the building blocks for behavior prediction, service design and system …
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Mining sophisticated patterns for classification and correlation analysis
Pattern mining has been a hot issue since it was first proposed for market basket analysis. Even though pattern mining is one of the oldest topic in data mining domain, there are still many ongoing challenges to overcome on this subject since the scale of the data size is getting bigger and the …
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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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User behavior pattern based security provisioning for distributed systems
… of user activities. Identifying user behavior patterns by analyzing audit logs is challenging. Lacking a general user behavior pattern model restricts the effective usage of data mining techniques. Limited access to real world audit logs due to privacy concerns also blocks user behavior …
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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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CASM: searching context-aware sequential patterns iteratively
Many applications are interested in mining context-aware sequential patterns such as opinions, common navigation patterns, and product recommendations. However, traditional sequential pattern mining algorithms are not effective to mine such patterns. We thus study the problem of searching …
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Harmonizing data mining and static analysis to tackle hardware and system level verification
… on STAR, a technique for generating input vector patterns for all paths of an RTL design using RTL symbolic execution. To attack the path explosion problem in STAR, we present HYBRO and the symbolic state caching method. HYBRO uses branch coverage metric to guide the path exploration. It is a …
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