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Showing 1 to 6 of 6 for “"Frequent Itemset Mining"”.

  1. Parallel Trie-based Frequent Itemset Mining on Graphics Processors

    Yao, Jay Junjie

    cuny Repository record

  2. A distributed approach to Frequent Itemset Mining at low support levels

    Clark, Neal2014

    Frequent Itemset Mining, the process of finding frequently co-occurring sets of items in a dataset, has been at the core of the field of data mining for the past 25 years. During this time the datasets have grown much faster than the algorithms capacity to process them. Great progress was made at …

    uvic Repository record

  3. Strip-Miner: Automatic Bug Detection in Large Software Code with Low False Positive Rate

    Ibrar, FahadVirginia Tech2020

    … simple dependency analysis of code with a data mining technique "frequent itemset mining" to reduce the false positive rate. We adopt a two phase approach 1) finding the potential bugs and 2) filtering the false positive ones. In the first phase we extract code elements and dependencies among …

    vt Repository record

  4. New Approaches to Frequent and Incremental Frequent Pattern Mining

    Bicer, MehmetThe Graduate School and University Center of The City University of New York2020

    <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 …

    cuny-grad Repository record

  5. Scaling data mining activities on very large datasets

    GRAND, ALBERTOPolitecnico di Torino2013

    … the issue of enhancing the scalability of data mining techniques, with specific emphasis on association rule and frequent itemset mining. In particular, it proposes a scalable itemset mining approach relying on (i) a persistent (disk-based) representation of the transactional data, (ii) ad-hoc …

    poli-torino Repository record

  6. API Knowledge Guided Test Generation for Machine Learning Libraries

    Narayanan, Arunkaleeshwaran2022

    … we propose a set of 18 linguistic rules for mining API constraints from the API documents. Then, we use the frequent itemset mining technique to mine the API usage patterns from a large corpus of machine learning API related code fragments collected from SO. Finally, we use the above two …

    york Repository record