K-means Nonhierarchical Cluster and Dbscan Outlier Detection In the Grouping of Stock Issuers

Abstract

Group analysis aims to group objects based on similar characteristics so that they are in one group homogeneous and between groups heterogeneous. Study this aim group issuer share in Indonesia based on volatility, liquidity, and market capital. This study uses the non-hierarchical K-Means Clustering method, because the number of samples is big and the number of groups are known. The K-Means Clustering grouping method produces as many as 6 groups with different characteristics. 2. Group 1 consists of stock issuers with quite high volatility and liquidity. The characteristic of group 2 is that it consists of stock issuers with the lowest volatility. Big Capital is the nickname for group 3 because it has market capital or the asset value is very large among all groups and the volatility is very small, and liquid. In Group 4, stock issuers have the highest volatility and the lowest liquidity. Results of profile interpretation in group 5, issuer’s stocks have the highest liquidity and market capital is quite low. Share issuers in group 6 have the volatility highest. Group 3 is recommended as an option for investing. Because, having market capital or large asset values, liquid, and volatility is low enough to minimize risk. The originality of this research is that there is no combination of methods between grouping in fields with k-means clustering and detection of sales with DBSCAN, especially in the field of issuer share in Indonesia.