Visual Analysis of E-Commerce User Behavior Based on Log Mining

Table 2

Cluster analysis results using the optimized K-means algorithm.

Type		Cluster
		1	2	3	4	5	6
Click	User_number	10,000	162	842	2657	4574	1765
	Clicks	1155	8205	3731	1637	597	264
Collect	User_number	10,000	15	460	55	1428	8042
	Favorites	25	1287	223	60	27	11
Cart	User_number	8890	389	445	47	6116	1893
	Goods	25	218	27	25	24	11
	Duration	28	38	145	365	56	9
Payment	User_number	10,000	55	1428	15	460	8042
	Payments	12	150	36	28	20	7
Commodity	User_number	10,000	356	122	755	2430	6337
	Types_of_goods	12	55	35	20	14	7
Churn_rate	User_number	10,000	101	1236	2638	2048	3977
	Payments	12	2	11	15	12	8
	Churn_rate	0.1184	0.7328	0.3124	0.1714	0.047	0.032