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  2. GSP algorithm - Wikipedia

    en.wikipedia.org/wiki/GSP_Algorithm

    The frequent 2-sequences are used to generate the candidate 3-sequences, and this process is repeated until no more frequent sequences are found. There are two main steps in the algorithm. Candidate Generation. Given the set of frequent (k-1)-frequent sequences F k-1, the candidates for the next pass are generated by joining F(k-1) with itself ...

  3. Rough set - Wikipedia

    en.wikipedia.org/wiki/Rough_set

    Rough set-based data analysis methods have been successfully applied in bioinformatics, economics and finance, medicine, multimedia, web and text mining, signal and image processing, software engineering, robotics, and engineering (e.g. power systems and control engineering). Recently the three regions of rough sets are interpreted as regions ...

  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. Sequential pattern mining - Wikipedia

    en.wikipedia.org/wiki/Sequential_Pattern_Mining

    Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. [ 1 ] [ 2 ] It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity.

  6. Rule induction - Wikipedia

    en.wikipedia.org/wiki/Rule_induction

    Data mining in general and rule induction in detail are trying to create algorithms without human programming but with analyzing existing data structures. [ 1 ] : 415- In the easiest case, a rule is expressed with “if-then statements” and was created with the ID3 algorithm for decision tree learning.

  7. Alpha algorithm - Wikipedia

    en.wikipedia.org/wiki/Alpha_algorithm

    The α-algorithm or α-miner is an algorithm used in process mining, aimed at reconstructing causality from a set of sequences of events. It was first put forward by van der Aalst, Weijters and Măruşter. [1] The goal of Alpha miner is to convert the event log into a workflow-net based on the relations between various activities in the event log.

  8. Underground hard-rock mining - Wikipedia

    en.wikipedia.org/wiki/Underground_hard-rock_mining

    Cut and fill mining is an expensive but selective method, with the advantages of low ore loss and dilution. [4] Drift and fill is similar to cut and fill, except that it is used in ore zones, which are wider than the method of drifting will allow to be mined. In this case, the first drift is developed in the ore, and is backfilled using ...

  9. OPTICS algorithm - Wikipedia

    en.wikipedia.org/wiki/OPTICS_algorithm

    The image above illustrates this concept. In its upper left area, a synthetic example data set is shown. The upper right part visualizes the spanning tree produced by OPTICS, and the lower part shows the reachability plot as computed by OPTICS. Colors in this plot are labels, and not computed by the algorithm; but it is well visible how the ...

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    bench and fill mining method in python 1 and 2 book set by example pdf