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。

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来自 University of Illinois at Urbana-Champaign 的课程

Pattern Discovery in Data Mining

194 个评分

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.

- Jiawei HanAbel Bliss Professor

Department of Computer Science

In this unit, we're going to study Mining and

Aggregating Patterns over Multiple Trajectories.

When we study spatial and

temporal patterns, an important pattern is trajectory pattern.

That means you look at the points, the objects,

the moving along the spatial map, along with time.

So we call these the trajectories.

What we want to find are trajectory patterns.

One interesting trajectory pattern mining method

called partition-based trajectory pattern mining.

They are mining T-patterns, T means trajectory.

This is a work done by a group of Italian researchers.

They published in KDD 2007 called Trajectory Pattern Mining.

Their mining method essentially is a really good study like busy

traffic in the city.

You can think a city can't be partitioned into

equal width grids to obtain regions of interest.

For example, one grid may represent the museum,

the other grid may represent the railway or campus.

Then when you study the busy traffic, they go along the route.

You will be able to find either what hours or how much time

you will find a very busy traffic and how long it may take to reach the other side.

So, what we can do is we can transform each input's

trajectory into a time annotated symbolic sequence.

For example, you may transform one location is a railway station.

Another location is Castle Square.

The third location could be museum.

And based on a time, you may say from the railway station, it takes about 15

minutes to reach Castle Square and takes 2 hours and 15 minutes to reach museum.

And we can use a constraint-based sequential pattern mining because

each grid is matched into a symbolic sequence.

So the finer you can use the symbolic sequence to represent

the whole trajectory and try to find trajectory pattern is trying

to use constraint-based sequential pattern mining.

The constraints can be the range of time delay.

Then, the constraint-based sequential pattern mining

results can be mapped onto the map to show how much time it may take

from one location going to the other location.

That kind of matching T-pattern will be something like (x0,

y0) after alpha time delay, you will reach (x1, y1) points.

This will get into explicit representation of the pattern.

Then another interesting group of studies are detecting moving object clusters.

That means, you may think trucks.

You may think animals.

They are moving together,

you may want to find their moving object clusters in this sense.

The first one, definition is flock.

Both flock and

a convoy require k consecutive time stamp in order to find patterns.

The flock essentially is they require at least m entities are within

the circular region of radius r, and they are moving together in the same direction.

Then we call these the flock pattern.

But a flock pattern is a little too rigid in the sense they require

at least m entities moving in k consecutive time stamps.

And it's within their movement are within the circular region of radius r,

they are relative distances.

And sometimes this radius r is too rigid.

The convoy definition is using density-based clustering.

They don't have to be within radius r.

They can be tighter, or they can be a little looser.

As long as they form a density-based clustering,

you'll be able to find those m entities, and in the k consecutive

time stamps, they move together, you can think they are convoy patterns.

However, such movement constraints are still very rigid

in the sense both require k consecutive time stamps.

We may think like animal movements as certain time stamp,

the animal may not be so closely clustered together.

They may spread around to graze or to do other things.

In that sense, we may relax this k consecutive time stamps

to allow at certain time stamps, they probably are quite far apart.

But on the other time stamps,

you will be able to find they are very closely moving together.

In that sense, we define such pattern as swarm.

Swarm means the moving objects may not need to be close together,

all the consecutive time stamps.

Of course, to find such movement pattern, it will be more

costly than finding flocks and convoys because the pattern is more relaxed.

Some efficient algorithm has been developed to mine such pattern.

The paper was published in VLDB 2010.

Now we look at another trajectory pattern is during clustering try to find them.

We call trajectory clustering.

This could be useful in, for example,

try to find land, try to forecast a hurricane landfall.

If you overlay many years of hurricane together,

you may find they may form very close clusters.

However, if you try to take the whole hurricane paths as inseparable,

you will not be able to find such patterns, just because at

certain points of time, these hurricanes may already nicely.

But at other time, they may become more spreading

because they were influenced by different flow of the air.

So try to find such patterns.

We will propose a partitioning and a grouping approach.

Partitioning means you will first chop these trajectories, for

each trajectory, you will chop them into a sequence of segments, okay?

Then after chopping these into sequence of segments, you will be able to find for

certain fragments, they are moving in the same direction.

They may form nice patterns.

You can group them together as trajectory clusters.

Then how we can nicely find such patterns?

For partitioning, you can use minimum description lens principle called MDL.

The MDL, the general philosophy is,

you try to use minimum number of points, but

maximally reflect the real trajectory paths.

That means instead of thinking you use many, many small fragments,

which are too costly, you try to use less of them.

But you don't want to use too few, because you will distort the picture.

You will try to maximally approximate the real trajectory pass.

In that sense you may say I use minimum description length principle

when the trajectory started turning in the sharper angle,

I would try to say these should be separate points.

Then you may find smaller number of fragments, but

maximum preserve the shape of the trajectory.

So this is interesting algorithm published in SIGMOD 2007 called

Trajectory Clustering: A Partition-and-Group Framework.

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