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【導(dǎo)讀】的缺點(diǎn)通過實(shí)驗(yàn)驗(yàn)證，這些敏感的因素對(duì)聚類結(jié)果具有哪些影響。本文的主要任務(wù)是實(shí)現(xiàn)。本文主要介紹了聚類分析，包括它各個(gè)方面的性能指標(biāo)測量函數(shù)和常見的聚類方法，行了六次試驗(yàn)，分別進(jìn)行實(shí)驗(yàn)這兩個(gè)初始條件的不同會(huì)對(duì)聚類結(jié)果有哪些影響。largedatabases．InPror．1996ACM-SlGMODhat．Conf．ManagementofData，Montreal。以為我們改變聚類效率提供參考。分析，得出本文需要驗(yàn)證的結(jié)論。：2021年12月——2021年01月；：2021年01月——2021年02月；還沒有公司企業(yè)專門從事聚類分析的研究，相對(duì)于外國來說起步較晚。各大科研機(jī)構(gòu)與高。校對(duì)聚類的研究主要是對(duì)其算法設(shè)計(jì)并實(shí)現(xiàn)，以此為基礎(chǔ)對(duì)算法改進(jìn)。目前人們已經(jīng)在統(tǒng)。數(shù)據(jù)量的數(shù)據(jù)庫會(huì)造成結(jié)果的不穩(wěn)定性，可伸縮性強(qiáng)的算法就亟待的研發(fā)出來。影響很明顯，使用戶在輸入時(shí)加大了分析的工作難度，很難與控制。維的聚類算法才是目前成熟的應(yīng)用的算法，一旦高維數(shù)據(jù)需要聚類處理，這就是一個(gè)難題，要按約束條件又要按聚類要求實(shí)現(xiàn)，是很有壓力和挑戰(zhàn)的一項(xiàng)任務(wù)。
【正文】 ontaining nested tables, such as a customer list followed by a nested purchases table. If a nested table exists in the dataset, each nested key (such as a product in the purchases table) is considered an item. The Microsoft Association algorithm also finds rules associated with itemsets. A rule in an association model looks like A, B=C (associated with a probability of occurring), where A, B, C are all frequent itemsets. The 39。=39。 implies that C is predicted by A and B. The probability threshold is a parameter that determines the minimum probability before a rule can be considered. The probability is also called confidence in data mining literature. Association models are also useful for cross sell or collaborative filtering. For example, you can use an association model to predict items a user may want to purchase based on other items in their basket. Microsoft Sequence Clustering The Microsoft Sequence Clustering algorithm analyzes sequenceoriented data that contains discretevalued series. Usually the sequence attribute in the series holds a set of events with a specific order (such as a click path). By analyzing the transition between states of the sequence, the algorithm can predict future states in related sequences. The Microsoft Sequence Clustering algorithm is a hybrid of sequence and clustering algorithms. The algorithm groups multiple cases with sequence attributes into segments based on similarities of these sequences. A typical usage scenario for this algorithm is Web customer analysis for a portal site. A portal Web site has a set of affiliated domains such as News, Weather, Money, Mail, and Sport. Each Web customer is associated with a sequence of Web clicks on these domains. The Microsoft Sequence Clustering algorithm can group these Web customers into moreorless homogenous groups based on their navigations patterns. These groups can then be visualized, providing a detailed understanding of how customers are using the site. Microsoft Neural Network In Microsoft SQL Server 2021 Analysis Services, the Microsoft Neural Network algorithm creates classification and regression mining models by constructing a multilayer perceptron work of neurons. Similar to the Microsoft Decision Trees algorithm provider, given each state of the predictable attribute, the algorithm calculates probabilities for each possible state of the input attribute. The algorithm provider processes the entire set of cases , iteratively paring the predicted classification of the cases with the known actual classification of the cases. The errors from the initial classification of the first iteration of the entire set of cases is fed back into the work, and used to modify the work39。s performance for the next iteration, and so on. You can later use these probabilities to predict an oute of the predicted attribute, based on the input attributes. One of the primary differences between this algorithm and the Microsoft Decision Trees algorithm, however, is that its learning process is to optimize work parameters toward minimizing the error while the Microsoft Decision Trees algorithm splits rules in order to maximize information gain. The algorithm supports the prediction of both discrete and continuous attributes. Microsoft Linear Regression The Microsoft Linear Regression algorithm is a particular configuration of the Microsoft Decision Trees algorithm, obtained by disabling splits (the whole regression formula is built in a single root node). The algorithm supports the prediction of continuous attributes. Microsoft Logistic Regression The Microsoft Logistic Regression algorithm is a particular configuration of the Microsoft Neural Network algorithm, obtained by eliminating the hidden layer. The algorithm supports the prediction of both discrete and continuous attributes. 出自： 數(shù)據(jù)挖掘教程 作者：塞思保羅 杰米 麥克倫 唐昭輝 斯科特 歐俉桑
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