ee.Clusterer.wekaCobweb
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.
| Usage | Returns |
|---|
ee.Clusterer.wekaCobweb(acuity, cutoff, seed) | Clusterer |
| Argument | Type | Details |
|---|
acuity | Float, default: 1 | Acuity (minimum standard deviation). |
cutoff | Float, default: 0.002 | Cutoff (minimum category utility). |
seed | Integer, default: 42 | Random number seed. |
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[[["Implements the Cobweb clustering algorithm for incremental conceptual clustering."],["Utilizes acuity and cutoff parameters to control cluster formation based on standard deviation and category utility."],["Offers flexibility in initialization through a user-defined random number seed."],["Based on research by Fisher (1987) and Gennari, Langley, & Fisher (1990) in machine learning and artificial intelligence."]]],["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"]]