GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data

Ruotsalainen, Marja; Ala-Kleemola, T.; Visa, Ari

doi:10.1109/cidm.2007.368920

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…Ruotsalainen et al [77] design the Gais genetic algorithm to detect interleaved patterns in an unsupervised learning fashion. Other approaches have been proposed to mine discontinuous patterns [73], in different types of sequence datasets and to allow variations in occurrences of the patterns [78].…”

Section: Behavioral Pattern Discoverymentioning

confidence: 99%

Mining the home environment

Cook

Krishnan

2014

J Intell Inf Syst

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Individuals spend a majority of their time in their home or workplace and for many, these places are our sanctuaries. As society and technology advance there is a growing interest in improving the intelligence of the environments in which we live and work. By filling home environments with sensors and collecting data during daily routines, researchers can gain insights on human daily behavior and the impact of behavior on the residents and their environments. In this article we provide an overview of the data mining opportunities and challenges that smart environments provide for researchers and offer some suggestions for future work in this area.

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Section: Behavioral Pattern Discoverymentioning

confidence: 99%

Mining the home environment

Cook

Krishnan

2014

J Intell Inf Syst

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“…Ruotsalainen et al [41] design the Gais genetic algorithm to detect interleaved patterns in an unsupervised learning fashion. Other approaches have been proposed to mine discontinuous patterns [7],[34],[50], in different types of sequence datasets and to allow variations in occurrences of the patterns [38].…”

Section: Related Workmentioning

confidence: 99%

Learning a taxonomy of predefined and discovered activity patterns

Krishnan

Cook

Wemlinger

2013

Journal of Ambient Intelligence and Smart Environments

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“…A Genetic Algorithm based method for Interleaved Sequential pattern detection(GAIS) from event sequences was suggested by Marja Ruotsalainen, et al [4]. GAIS assumed the existence of models to detect the required kind of patterns from the event sequences.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Concurrent and Interleaved Activities in a Smart Environment using Finite Automata

Ali¹,

Amalarethinam²

2014

IJCA

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People have to do a number of activities in their day-to-day life. Though there are some activities that can be done only sequentially, most of the day-to-day activities do not impose such restriction. People very often tend to multitask with such activities. They interleave activities or go about them sequentially or concurrently. So, to be useful in real life situations, an activity recognition system must be able to recognize activities irrespective of how the user performs the activities. This paper proposes a novel simple approach that can be used to recognize sequential, interleaved and concurrent activities efficiently. The proposed method is tested with a publicly available dataset and is producing very promising results.

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GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data

Cited by 16 publications

References 20 publications

Mining the home environment

Mining the home environment

Learning a taxonomy of predefined and discovered activity patterns

Recognition of Concurrent and Interleaved Activities in a Smart Environment using Finite Automata

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