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Analyzing Data Streams Using a Dynamic Compact Stream Pattern Algorithm

Authors:
Ayodeji Oyewale
Chris Hughes
Mohammed Saraee

Keywords: Data Mining; Frequent Pattern (FP); Stream data; Compact Pattern Stream (CPS) & Interactive Mining, Path Adjustment Method (PAM), Branch Sort, Merge Sort Algorithm.

Abstract:
In order to succeed in the global competition, organisations need to understand and monitor the rate of data influx. The acquisition of continuous data has been extremely outstretched as a concern in many fields. Recently, frequent patterns in data streams have been a challenging task in the field of data mining and knowledge discovery. Most of these datasets generated are in the form of a stream (stream data), thereby posing a challenge of being continuous. Therefore, the process of extracting knowledge structures from continuous rapid data records is termed as stream mining. This study conceptualizes the process of detecting outliers and responding to stream data. This is done by proposing a Compressed Stream Pattern algorithm, which dynamically generates a frequency descending prefix tree structure with only a single-pass over the data. We show that applying tree restructuring techniques can considerably minimize the mining time on various datasets.

Pages: 28 to 32

Copyright: Copyright (c) IARIA, 2018

Publication date: July 22, 2018

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-654-5

Location: Barcelona, Spain

Dates: from July 22, 2018 to July 26, 2018