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  2. Apriori algorithm - Wikipedia

    en.wikipedia.org/wiki/Apriori_algorithm

    Apriori [1] is an algorithm for frequent item set mining and association rule learning over relational databases.It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

  3. Association rule learning - Wikipedia

    en.wikipedia.org/wiki/Association_rule_learning

    For example a 10^4 frequent 1-itemset will generate a 10^7 candidate 2-itemset. The algorithm also needs to frequently scan the database, to be specific n+1 scans where n is the length of the longest pattern. Apriori is slower than the Eclat algorithm. However, Apriori performs well compared to Eclat when the dataset is large.

  4. Frequent pattern discovery - Wikipedia

    en.wikipedia.org/wiki/Frequent_pattern_discovery

    Frequent pattern discovery (or FP discovery, FP mining, or Frequent itemset mining) is part of knowledge discovery in databases, Massive Online Analysis, and data mining; it describes the task of finding the most frequent and relevant patterns in large datasets. [1] [2] The concept was first introduced for mining transaction databases. [3]

  5. Affinity analysis - Wikipedia

    en.wikipedia.org/wiki/Affinity_analysis

    There are two important metrics for performing the association rules mining technique: support and confidence. Also, a priori algorithm is used to reduce the search space for the problem. [1] The support metric in the association rule learning algorithm is defined as the frequency of the antecedent or consequent appearing together in a data set ...

  6. GSP algorithm - Wikipedia

    en.wikipedia.org/wiki/GSP_Algorithm

    GSP algorithm (Generalized Sequential Pattern algorithm) is an algorithm used for sequence mining. The algorithms for solving sequence mining problems are mostly based on the apriori (level-wise) algorithm. One way to use the level-wise paradigm is to first discover all the frequent items in a level-wise fashion.

  7. Massive Online Analysis - Wikipedia

    en.wikipedia.org/wiki/Massive_Online_Analysis

    Frequent pattern mining. Itemsets [11] Graphs [12] Change detection algorithms [13] These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time. MOA supports bi-directional interaction with Weka. MOA is free software released under the GNU GPL.

  8. US weekly jobless claims fall; third-quarter GDP growth ...

    www.aol.com/news/us-weekly-jobless-claims-fall...

    The claims data covered the week during which the government surveyed businesses for the nonfarm payrolls component of December's employment report. Claims rose marginally between the November and ...

  9. Examples of data mining - Wikipedia

    en.wikipedia.org/wiki/Examples_of_data_mining

    An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing." [12] In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a ...