towards efficient techniques to discover moving flock patterns: how can association rules help?

8/6/2019 Towards efficient techniques to discover moving flock patterns: How can association rules help?

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Towards efficient techniques to discovermoving flock patterns: How can

association rules help?

Andrés Oswaldo Calderón RomeroGEM MSc Program

[email protected]



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Motivation

More and more geographic and temporal data... more and

more difficult to analyse them...

Spatio-temporaldatasets

GPS

GPS

RFID

RFID

Wireless sensor

networks

Wireless sensor networks

Remote sensing

Remote sensing

Mobile phones

Mobile phonesGeneral surveys

General surveys



Motivation

An urgent need of new analysis

methods...

Traditional spatial analyses is not an

answer.

Scalability

Accuracy

Traditional data mining...

Spatial is Special

Spatio-temporal

datasets



MotivationGeographic Knowledge Discovery (GKD) and Spatial Data Mining (SDM)

Guo and Mennis (2009) enumerate 4 categories of SDM:

Clustering

Classification

Association rules

Geo-visulization



MotivationTrajectory datasets

They collect the location of moving

entities for a given time duration.

Relevant due to the characteristics

of the objects of interest: animals,

pedestrians, vehicles or natural

phenomena.

The Space-time cube.

GIP Department, ITC



MotivationMoving flock patterns

Vieira et al. (2009) define moving flock patterns as sets of entities moving in

the same direction while being close to each other for the duration of a given

time interval.

(after Vieira et al., 2009)



MotivationHow can association rules help?

Association rule learning is a well researched method for discovering

interesting relations among variables in large databases.

Used commonly in large transactional databases of big supermarkets tounderstand how products are purchased together (Larose, 2005).

A typical association rule in this case is:

“80% of people who bought potato chips

and beer, also bought diapers”

Han and Kamber, 2006



Motivation

How can association rules help?

Association rules and moving flock patterns present interesting similarities:

The main goal is understand how entities or variables 'go together'.

Sets of locations in trajectory datasets are comparable with sets of

products in transactional databases.

They share similar problems related to magnitude and pattern

generation.



Problem statement

Current algorithms to extract useful moving flock patterns from trajectory

datasets are time-consuming. There are two main issues:

Proposed algorithms scan the point sets repeatedly, so execution time

depends highly on the size of the database.

They are based on candidate generation. To handle and analyse

possible patterns have a direct repercussion on the algorithm’s

performance.

Association rule techniques have faced similar size and candidate generation

issues, though without considering spatial context.



Research identification

This investigation aims to define a new methodology to mine moving flock

patterns in trajectory datasets based on a new paradigm, the frequent pattern

approach (Han et al., 2004), used successfully in the association rule task,

aiming to reduce the number of scans and candidate patterns.



Research objectives

In order to accomplish this purpose there are three main objectives:

1. To define the appropriate procedure to fit the concept of moving

flock patterns in the association rules methodology.

2. To provide a general framework for pattern recognition in moving object

datasets based on the methodology proposed.

3. To test the performance of the resulting algorithm using study cases

with real datasets.



Related Work

Previous works in detection of moving flock patterns are reported by:

Gudmundsson et al. (2006) and Benkert et al. (2008) introduced the use of

disks with a predefine ratio (although with an infinite number of possible

locations).

Vieira et al. (2009) revealed that a discrete number of locations to place the

disk is possible (polynomial time solution). They proposed the Basic Flock

Evaluation (BFE) algorithm.

Wachowicz et al. (2010) proposed a new moving flock pattern definition anddeveloped the corresponding algorithm. They use each point as the centre of

the disk, reducing the number of combination but it will not find a complete set

of flocks.



Related Work

Traditional association rule techniques have treated the repeated scanning

and candidate generation using different approaches:

Agrawal and Srikant (1994) have applied pruning techniques based on the

downward-closure property (a frequent pattern subset should be frequent).

Timaran and Millan (2005) calculated all possible combinations for each

record and then stored them in efficient data structures to be counted.

Han et al. (2004) proposed the frequent pattern approach: an intermediate

layer which organized and compressed the records in a compact datastructure without candidate generation.



BFE Algorithm implementation...

Vieira et al, 2009



Can AR help???

Each trajectory can be seen as sets of consecutive disks...

Flocks can be seen as subsets of disks 'happening together'...



What is next...'Transactionizing' the spatial dataset and test the frequent pattern approach

algorithm (FP-Growth) with the generated database.

Post-processing to the algorithm's output. Checking time consecutivenessand Consistency.

Is it possible avoid the disk generation step using a grid???



Thanks for your attention...

“Reasonable people adapt themselves to the world. Unreasonable

people attempt to adapt the world to themselves. All progress,

therefore, depends on unreasonable people.”

George Bernard Shaw

towards efficient techniques to discover moving flock patterns: how can association rules help?

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