国科大数据挖掘刘莹作业2.pdf

可用于刘莹数据挖掘课程的复习，考试重点！

超级详细，每个步骤都很详细。 1、Suppose that a data warehouse consists of four dimensions, date, spectator, location, and game, and two measures, count and charge, where charge is the fare that a spectator pays when watching a game on a given date. Spectators may be students, adults, or seniors, with each category having its own charge rate.

国科大 刘莹 数据挖掘课程的期末考试的一些问答题比较麻烦，楼主在准备考试的时候结合前几年的往年提整理了一份自己的答案。可以给将来学这个课程的学弟学妹们一些参考。

国科大数据挖掘刘莹第三次作业

1．Consider the data set shown in Table 1（min_sup = 60%, min_conf=70%）. (a) Find all frequent itemsets using Apriori by treating each transaction ID as a market basket. (b) Use the results in part (a) to compute the confidence for the association rules {a, b}{c} and {c}{a, b}. Is confidence a symmetric measure? (c) List all of the strong association rules (with support s and confidence c) matching the following metarule, where X is a variable representing customers, and itemi denotes variables representing items (e.g. “A”, “B”, etc.): 2．Consider the data set shown in Table 1（min_sup = 60%）. (a) Find all frequent itemsets using FP-Growth. Please present all the FP-trees and all the conditional pattern bases. (b) Compare the efficiency of Apriori and FP-Growth.

基于Wasserstein距离和_省略_布鲁棒优化的清洁能源电源规划方法_汪荣华_刘洋_朱觅_刘莹_李奥_苟竞_唐权_胥威汀_李婷_王云玲.caj

国科大2020年刘莹数据挖掘大作业 天体光谱数据分类。我们小组是利用resnet对一维光谱数据进行分类。压缩包里面有我们的实验报告，应该还算是挺详细的

2020年国科大(中科院)刘莹数据挖掘课程期末考试试卷

国科大刘莹老师数据挖掘课件思维导图.rar