面向大数据的并行聚类算法在股票板块划分中的应用

doi:10.11959/j.issn.2096-0271.2015036

Abstract

Abstract:

For the operating performance of listed corporations reflects the value of stock investment to a certain extent, financial index reflecting the operating performance of listed corporations was taken as the evaluation index of stock investment value, and for the first time the parallel clustering algorithms for big data both K-means and fuzzy K-means of Mahout were used to cluster nearly 2 600 stock of China’s A shares market according to their financial index, afterwards the clustering results of these two algorithms under different distance metrics were compared.Experimental results show that the clustering quality of K-means algorithm adopting Tanimoto distance metric is the best.Therefore, this result can be used as the final result of the division of stock, which can provide a reference for the investment decision.

Key words: inancial index, parallel clustering algorithm, K-means, fuzzy K-means, division of stock

CLC Number:

TP311.13

Mo Hai, Yihan Niu, Yuejin Zhang. Application of Parallel Clustering Algorithms for Big Data in the Division of Stock[J]. Big Data Research, 2015, 1(4): 9-17.

Figures/Tables 6

References 9

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算法	聚类个数	迭代次数	收敛阈值	模糊参数
K-means	20	50	0.5	/
模糊K-means	20	50	0.5	2

距离度量方法	迭代次数	收敛时间/min	聚类间密度	聚类内密度
欧几里得距离	20	6.57	0.093	0.553
平方欧几里得距离	15	4.951	0.091	0.554
曼哈顿距离	17.3	5.631	0.099	0.544
余弦距离	1.7	0.758	0.077	0.555
谷本距离	4.3	1.602	0.045	0.557

距离度量方法	迭代次数	收敛时间/min	聚类间密度	聚类内密度
欧几里得距离	3.7	1.484	0.12	0.549
平方欧几里得距离	3.7	1.477	0.05	0.581
曼哈顿距离	5	1.929	0.124	0.544
余弦距离	1	0.585	0.115	0.573
谷本距离	7.7	2.85	0.063	0.489

类别	股票数量/支	盈利能力	成长能力	股东获利能力	偿债能力	资产管理质量	平均标准差
1	20	0.226	0.053	0.053	0.063	0.191	0.117
2	13	0.113	0.111	0.111	0.038	0.235	0.122
3	12	0.168	0.047	0.047	0.179	0.150	0.118
4	17	0.256	0.386	0.386	0.311	0.374	0.343
5	11	0.168	0.104	0.104	0.068	0.262	0.141
6	16	0.218	0.115	0.115	0.206	0.253	0.182
7	16	0.161	0.228	0.228	0.751	0.267	0.327
8	10	0.139	1.026	1.026	0.083	0.257	0.506
9	20	0.186	0.146	0.146	0.480	0.139	0.219
10	14	0.180	0.055	0.055	0.032	0.237	0.112
11	17	0.431	0.123	0.123	0.081	0.250	0.201
12	20	10.133	7.617	7.617	10.451	7.635	8.690
13	33	0.153	0.433	0.433	0.141	0.253	0.283
14	29	0.126	0.095	0.095	0.404	0.295	0.203
15	12	0.173	0.048	0.048	0.388	0.236	0.179
16	34	0.209	0.215	0.215	0.266	0.793	0.340
17	16	0.162	0.053	0.053	0.534	0.132	0.187
18	19	0.210	0.053	0.053	0.147	0.151	0.123
19	22	0.175	0.694	0.694	0.421	0.356	0.468
20	38	0.296	1.631	1.631	0.117	0.244	0.784
21	21	0.455	0.055	0.055	0.046	0.243	0.171
22	12	0.130	0.238	0.238	0.055	0.285	0.189
23	17	0.781	0.058	0.058	0.456	0.379	0.346
24	22	0.188	0.078	0.078	0.044	0.235	0.125
25	11	0.108	0.068	0.068	0.116	0.323	0.137
26	16	0.400	0.275	0.275	0.157	0.359	0.293
27	23	0.237	0.117	0.117	0.320	0.335	0.225

类别	股票数量/支	盈利能力	成长能力	股东获利能力	偿债能力	资产管理质量	平均标准差
1	50	0.085	0.077	0.275	0.062	0.117	0.123
2	77	0.172	0.093	0.363	0.060	0.161	0.170
3	89	0.185	0.166	0.444	0.073	0.271	0.228
4	44	0.159	0.045	0.152	0.048	0.120	0.105
5	64	0.071	0.069	0.147	0.057	0.084	0.086
6	79	0.084	0.088	0.151	0.075	0.087	0.097
7	58	0.193	0.084	1.852	0.159	0.486	0.555
8	76	0.278	0.107	0.297	0.117	0.256	0.211
9	62	0.069	0.045	0.135	0.106	0.088	0.088
10	43	0.105	0.057	0.160	0.043	0.088	0.091
11	30	0.047	0.029	0.096	0.039	0.051	0.052
12	35	0.053	0.038	0.079	0.039	0.050	0.052
13	31	0.070	0.031	0.097	0.154	0.057	0.082
14	87	0.123	0.068	0.217	0.102	0.098	0.122
15	57	0.058	0.043	0.166	0.055	0.063	0.077
16	48	0.109	0.108	0.244	0.070	0.139	0.134
17	52	0.070	0.047	0.196	0.037	0.095	0.089
18	77	0.112	0.079	0.192	0.078	0.113	0.115
19	41	0.066	0.061	0.097	0.040	0.082	0.069
20	33	0.093	0.045	0.211	0.146	0.101	0.119
21	67	0.221	0.174	0.588	0.091	0.679	0.351
22	54	0.074	0.028	0.105	0.036	0.075	0.064
23	55	0.094	0.048	0.157	0.094	0.091	0.097
24	59	0.043	0.038	0.096	0.039	0.061	0.055
25	35	0.196	0.082	0.480	2.096	2.965	1.164
26	80	0.057	0.058	0.093	0.055	0.065	0.066

Application of Parallel Clustering Algorithms for Big Data in the Division of Stock

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类别	特征	股票类别
第一类	具有较高的投资价值、较高的投资安全性	（7、8、13）
第二类	具有较高的投资价值、一般的投资安全性	（3、4、9、11、16、21）
第三类	具有较高的投资价值、较低的投资安全性	（1、5、6、12、22、23）
第四类	具有一般的投资价值、较高的投资安全性	（25）
第五类	具有一般的投资价值、一般的投资安全性	（26、18、20、2）
第六类	具有一般的投资价值、较低的投资安全性	（14、15、19）
第七类	具有较低的投资价值、一般的投资安全性	（17）
第八类	具有较低的投资价值、较低的投资安全性	（10、24）

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