6.4. Mining Semantics-Rich Movement Patterns

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来自 University of Illinois at Urbana-Champaign 的课程

Pattern Discovery in Data Mining

155 个评分

University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining

155 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 movement pattern

called Mining Semantics-Rich Movement Patterns.

We'll first see what is Frequent Movement Pattern?

Frequent Movement Pattern is a movement sequence, frequently appears

in the input trajectory database, but we care more on the semantics here.

For example, from home to Starbucks to office.

So maybe people, the home maybe different or office maybe different location,

but this pattern maybe interesting, even we can consider the home and

office could be just the same semantic units in different locations.

So comparing to Frequent Movement Pattern versus

Frequent Sequential Pattern, we can say,

they both are at finding frequent subsequences from input sequence database.

However, for mining frequent movement patterns,

we would like to think of similar places may need to be grouped together

collectively to form frequent subsequences from the semantic point of view.

Now, we look at Mining Semantics-Rich Movement Patterns.

The semantics-rich patterns means in addition to know how people move from

one region to another, we want to also understand the functions of the regions.

That means we want them see people moving from office to restaurant or

from home to gym.

So, we promote a two-step top-down mining approach.

The first step is try to find a set of a coarse patterns that

reflects people semantic level transitions.

That means as long as a reaching carry the similar semantic functions,

we may one to consider they are the same function or same entity.

Then in the step two, we split each coarse pattern into several

fine-grained ones by grouping similar movement snippets together.

This time, we'll consider spatial, temporal, besides the semantics.

We're going to consider spatial and temporal features more seriously.

A recent study published in VLDB 2014 called Splitter,

Mining Fine-Grained Sequential Patterns in

Semantic Trajectories is an interesting study in this direction.

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