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ee.Clusterer.wekaCobweb
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
Cobweb 聚类算法的实现。如需了解详情,请参阅以下内容:
D. Fisher(1987 年)。通过增量概念聚类获取知识。机器学习。2(2):139-172. 和 J. H. Gennari, P. Langley, D. Fisher(1990 年)。增量概念形成模型。人工智能。40:11-61.
用法 | 返回 |
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ee.Clusterer.wekaCobweb(acuity, cutoff, seed) | 聚类器 |
参数 | 类型 | 详细信息 |
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acuity | 浮点数,默认值:1 | 敏锐度(最小标准差)。 |
cutoff | 浮点数,默认值:0.002 | 截止值(最低类别效用)。 |
seed | 整数,默认值:42 | 随机数种子。 |
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最后更新时间 (UTC):2025-07-26。
[null,null,["最后更新时间 (UTC):2025-07-26。"],[[["\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. |"]]