Survey on Frequent Pattern Mining Algorithm for Kernel Trace

A. Tate, L. Bewoor
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引用次数: 3

Abstract

Kernel tracing facilitates to demonstrate various activities running inside the Operating System. Kernel tracing tools like LTT, LTTng, DTrace, FTrace provide details about processes and their resources but these tools lack to extract knowledge from it. Pattern recognition is a major field of data mining and knowledge discovery. This paper presents a survey of widely used algorithms like Apriori, Tree-projection, FPgrowth, Eclat for finding frequent pattern over the database. This paper presents a comparative study of frequent pattern mining algorithm and suggests that the FP-growth algorithm is suitable for finding patterns in kernel trace data.
核迹频繁模式挖掘算法综述
内核跟踪有助于演示在操作系统内部运行的各种活动。内核跟踪工具,如LTT、LTTng、DTrace、FTrace提供了进程及其资源的详细信息,但这些工具缺乏从中提取知识的能力。模式识别是数据挖掘和知识发现的一个重要领域。本文综述了Apriori、Tree-projection、FPgrowth、Eclat等常用的数据库频繁模式查找算法。本文对频繁模式挖掘算法进行了比较研究,认为FP-growth算法适合于在核跟踪数据中发现模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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