[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-07-26。"],[[["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"]]