6.5. Mining Periodic Movement Patterns

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来自 University of Illinois at Urbana-Champaign 的课程

Pattern Discovery in Data Mining

199 个评分

University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining

199 个评分

课程 4（共 6 门，Specialization Data Mining ）

这这一课程中，我们将学习数据挖掘的基本概念及其基础的方法和应用，然后深入到数据挖掘的子领域——模式发现中，学习模式发现深入的概念、方法，及应用。我们也将介绍基于模式进行分类的方法以及一些模式发现有趣的应用。这一课程将给你提供学习技能和实践的机会，将可扩展的模式发现方法应用在在大体量交易数据上，讨论模式评估指标，以及学习用于挖掘各类不同的模式、序列模式，以及子图模式的方法。

从本节课中

Module 3

Module 3 consists of two lessons: Lessons 5 and 6. In Lesson 5, we discuss mining sequential patterns. We will learn several popular and efficient sequential pattern mining methods, including an Apriori-based sequential pattern mining method, GSP; a vertical data format-based sequential pattern method, SPADE; and a pattern-growth-based sequential pattern mining method, PrefixSpan. We will also learn how to directly mine closed sequential patterns. In Lesson 6, we will study concepts and methods for mining spatiotemporal and trajectory patterns as one kind of pattern mining applications. We will introduce a few popular kinds of patterns and their mining methods, including mining spatial associations, mining spatial colocation patterns, mining and aggregating patterns over multiple trajectories, mining semantics-rich movement patterns, and mining periodic movement patterns.

6.1. Mining Spatial Associations4:40

6.2. Mining Spatial Colocation Patterns9:10

6.3. Mining and Aggregating Patterns over Multiple Trajectories9:30

6.4. Mining Semantics-Rich Movement Patterns3:05

6.5. Mining Periodic Movement Patterns7:27

与讲师见面

Jiawei Han
Abel Bliss Professor
Department of Computer Science

Now we study another interesting issue called Mining Periodic Movement Patterns.

Usually the sensor may register movement data, but

in many cases you may have very sparse data.

Just giving you a very interesting example.

For example, animal scientists or bird scientists,

they would like to study animal movements and bird movements.

They put censors on the body of those animals.

But remember the sensor usually is pretty small,

on the other hand it will last, it will need to be last for a long time.

Like, for a study of bird migration,

a sensor on the body of the bird is expected to last for a year.

So, how could a sensor be lasting for

a year without charging the battery?

The only thing you can think is we will get very sparse data,

like 24 hours we may only register one GPS location although

24 hours the birds may fly very far away.

Then if we want to find the periodicity,

the interesting thing could be it's very hard to find

the periodicity if there's not overlap, just because the data is so sparse.

Then just to look at how to, thinking about bird flying patterns,

we just think the bird, suppose this place is the nest of the bird, okay.

Then, if you just look at the bird,

how they exactly fly with a very sparse sensor data,

you'll not be able to find any meaningful periodic patterns of bird flying.

However, you may find every night,

every evening the birds were flying back to the nest.

[COUGH] In the morning it will fly out from the nest.

So, from the nest point of view or

from the cluster center point of view it does have some periodicity.

Then we may take such location as our reference spots.

That means the reference spots can be detected using density-based

method because they come back quite frequently.

Then with this, if we do just based on the nest,

we say when they're in the nest, when they're out of the nest,

so we will be able to find a nice and interesting periodic patterns.

That means the periodicity is more obvious

when we look at the binary sequence like in and out of the nest.

So that's interesting [INAUDIBLE] can see if we only think of

a nest the periodicity will be able to be detected for

each reference spot using Fourier Transform and auto-correlation.

Now we look at an interesting example on mining

periodic patterns with sparse data.

This is a real data sets about birds flying in North America.

So this actually accumulate three-year bird migration data,

they are very, very sparse.

However, if you based on the dense points,

you take those dense regions as reference spots,

you will be able to find a periodicity because in

the winter they're going to fly down to the South.

In the summer they may fly back to the North.

In the middle they may fly some places to have some other activities,

so you will find a few interesting reference spots.

Once we detect periodicity based on those reference points,

we will be able to summarize such patterns in a periodic way because we

find a period and we find the nice periodic patterns.

So this is an interesting study, based on the very sparse

data we will be able to mine periodic behaviors for moving objects.

Then the previous study we will assume the periodicity

can be detected because the period is sort of knowing, okay.

Then, in many cases, the time related data can be scattered and sparse.

For example, phone calls at a location, they could be very sparse.

And then for such sparse data, if we look at the time,

we will be able to map the calling time in the sequence

based on the time you're say 5, 13, 26, 29.

If you map this into the time sequence, it's very hard to find a periodicity.

Then what about if we can guess a right period, like t.

If t is 20, if we guess it right,

you probably can easily see they form a very dense cluster.

However, if you got a wrong guess on the period,

likely this mapping will be scattered along the whole period.

So that means the projection on the true period,

it will show highly skewed (clustered) distribution.

Based on this observation, some in-depth study and

algorithm have been developed to detect the true pattern

in a mining event periodicity from incomplete observations.

So in this whole session, we discussed a few interesting issues.

We studied Mining Spatial Associations, Mining Spatial Colocation Patterns,

Mining and Aggregating Patterns over Multiple Trajectories,

Mining Semantics-Rich Movement Patterns and Mining Periodic Movement Patterns.

In this session we actually used the following research papers.

If you would like to know more,

we strongly encourage you to read those research papers.

Thank you.

[MUSIC]

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