Role of clustering based on density to detect patterns of stock trading deviation

Alvida Mustika Rukmi, Soetrisno, Abirohman Wahid

Research output: Contribution to journalConference articlepeer-review

Abstract

The pattern of deviation patterns can be identified from the results of cluster transactions and transactions that are transaction irregularities, will be detected. DBSCAN as a density-based clustering algorithm forms clusters that agglomerate and make it easier to detect unclustered data, which is considered as data noise (data outlier). The nature of density in the data clamping process will make it easier to determine noise data objects.The DBSCAN has two parameters, Eps and MinPts. The values entered in both parameters play a role in forming clusters. Stock trading transactions are stated as data objects to be clustered. The noise from clustering with DBSCAN shows outlier transactions, which have diferrent pattern with ordinary transactions. In the results of this clustering, the stock transaction pattern which includes outliers is obtained, marking the close occurs. This result can help to detect stock price manipulation in outlier transactions carried out by securities brokers.

Original languageEnglish
Article number012053
JournalJournal of Physics: Conference Series
Volume1218
Issue number1
DOIs
Publication statusPublished - 31 May 2019
Event3rd International Conference on Mathematics; Pure, Applied and Computation, ICoMPAC 2018 - Surabaya, Indonesia
Duration: 20 Oct 2018 → …

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