{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","uuid":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","normalized_title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","isbns":["9787302517986"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":490,"navigation_url":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E6%8A%80%E6%9C%AF%E5%AF%BC%E8%AE%BA","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","page_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/search"},"page":{"printed_page":187,"pdf_page":203,"physical_index":203,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"b82e1172dddcef3c3525db875d3e31382ec1abd37cbc1e5e503bdd78c595e049"},"chapter":"第6章","content":"为了解释均值漂移，我们将考虑二维空间中的一组点，如图5-28所示。我们从一个以C点（随机选择）为中心，以半径r为核心的圆形滑动窗口开始。均值漂移是一种爬山算法，它包括在每一步中迭代地向更高密度区域移动，直到收敛。在每次迭代中，滑动窗口通过将中心点移向窗口内点的均值（因此而得名）来移向更高密度区域。滑动窗口内的密度与其内部点的数量成正比。自然，通过向窗口内点的均值移动，会逐渐移向点密度更高的区域。我们继续按照均值移动滑动窗口，直到没有区域在核内可以容纳更多的点。我们一直移动这个圆直到密度不再增加（即窗口中的点数）。这是通过许多滑动窗口完成的，直到所有的点位于一个窗口内。当多个滑动窗口重叠时，保留包含最多点的窗口。然后根据数据点所在的滑动窗口进行聚类。\n\n与K-Means聚类相比，这种方法不需要选择簇数量，因为均值漂移自动发现这一点。这是一个巨大的优势。它的缺点是窗口大小/半径的选择可能是不重要的。\n\n6.7.3 基于密度的聚类方法\n\nDBSCAN是一种基于密度的聚类算法，类似于均值漂移，但具有一些显著的优点。请看图6-31的例子。\n\n第6章 机器学习算法\n\n187","previous_page":186,"next_page":188}