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Implementation of the Cobweb clustering algorithm. For more information see:
D. Fisher (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning. 2(2):139-172. and J. H. Gennari, P. Langley, D. Fisher (1990). Models of incremental concept formation. Artificial Intelligence. 40:11-61.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\u003cp\u003eImplements the Cobweb clustering algorithm for incremental conceptual clustering.\u003c/p\u003e\n"],["\u003cp\u003eUtilizes acuity and cutoff parameters to control cluster formation based on standard deviation and category utility.\u003c/p\u003e\n"],["\u003cp\u003eOffers flexibility in initialization through a user-defined random number seed.\u003c/p\u003e\n"],["\u003cp\u003eBased on research by Fisher (1987) and Gennari, Langley, & Fisher (1990) in machine learning and artificial intelligence.\u003c/p\u003e\n"]]],["The core content details the implementation of the Cobweb clustering algorithm. It allows users to create a clusterer with the `ee.Clusterer.wekaCobweb` function. This function takes three arguments: `acuity` (minimum standard deviation, default 1), `cutoff` (minimum category utility, default 0.002), and `seed` (random number seed, default 42). The function returns a `Clusterer` object. References to academic papers by Fisher and Gennari, Langley, and Fisher are also provided for more information about the algorithm.\n"],null,["# ee.Clusterer.wekaCobweb\n\nImplementation of the Cobweb clustering algorithm. For more information see:\n\n\u003cbr /\u003e\n\nD. Fisher (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning. 2(2):139-172. and J. H. Gennari, P. Langley, D. Fisher (1990). Models of incremental concept formation. Artificial Intelligence. 40:11-61.\n\n| Usage | Returns |\n|---------------------------------------------------------------|-----------|\n| `ee.Clusterer.wekaCobweb(`*acuity* `, `*cutoff* `, `*seed*`)` | Clusterer |\n\n| Argument | Type | Details |\n|----------|-----------------------|--------------------------------------|\n| `acuity` | Float, default: 1 | Acuity (minimum standard deviation). |\n| `cutoff` | Float, default: 0.002 | Cutoff (minimum category utility). |\n| `seed` | Integer, default: 42 | Random number seed. |"]]
