1. FLOSCAN: An Artificial Life Based Data Mining Algorithm
A. Bellaachia
Computer Science Department
School of Engineering and Applied Sciences
George Washington University
Washington, DC 20052
A. Bellaachia
April 21, 2019

2. Outline Introduction Artificial Life Flocking Behavior
Flocking Parameters
FLOSCAN
Experimental Results
Conclusion & Future Work
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April 21, 2019

3. Artificial Life Behaviors
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April 21, 2019

4. Biologically inspired
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April 21, 2019

5. Flocking Behavior
Introduced by Reynolds in 1987 for computer graphics applications
Based on the flocking behavior of birds.
Each boid is defined by its direction, speed, the position of a set of birds (Boids) are related to the positions and velocities of its neighbors
Simple rules on individual boids yields a very interesting global behavior:
No leader, shape, global constraint.
A. Bellaachia
April 21, 2019

6. Flocking Behavior Flocking basic rules:
Separation: steer to avoid crowding local flock-mates or collide with neighboring boids.
Alignment: steer towards the average heading of local flock-mates.
Cohesion: steer to move toward the average position of local flock-mates.
A. Bellaachia
April 21, 2019

7. Flocking Behavior (Reynold’s Model)
Boid Parameters:
Velocity: refers to the combination of heading and speed.
Minimum distance
Angle: Boid’s vision 
Position
Maximum distance
A. Bellaachia
April 21, 2019

8. Vj (Or average velocity of neighborhood boids)
Flocking Model y
Vj (Or average velocity of neighborhood boids) Vi
VND
VAL  Vj
VAT
New Position  x
A. Bellaachia
April 21, 2019

9. Attraction: Where is the maximum distance between two boids.
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April 21, 2019

10. FLOSCAN: Flocking Model
Boids Movement
Velocity = Current Position
VAL (align Vi) = average of velocity of ( Boid i and Velocity of neighboring boid(s) (Vj) )
New Direction: VND = KND *(VAL+VAT)
No Separation y Vj 
VND
VAL= (Vi+Vj)/2 Vj
Vi: Velocity =
Current Position
VAT
New Position  x
A. Bellaachia
April 21, 2019

11. FLOSCAN: Flocking Model
Similar to the previous model except for VAL y
Velocity = Current Position
Ignore Vi
VAL (align Vi) = KAL*V
New Direction: VND = KND *(VAL+VAT)
No Separation Vj 
VND
VAL = KAL*Vj Vj
VAT
Vi: Velocity =
Current Position
New Position  x
A. Bellaachia
April 21, 2019

12. FLOSCAN: Attraction Alignment: Attraction:
Where K1 is constant
Note that Vj can also be the average velocity of the neighborhood boids.
Attraction:
Where is the maximum distance between two boids.
A. Bellaachia
April 21, 2019

13. FLOSCAN: New Direction & Position
Where KND is constant
New Position:
First Get the Speed vector:
Where
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April 21, 2019

14. FLOSCAN: New Position
The new position vector of boid i is calculated as follows:
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April 21, 2019

15. FLOSCAN: Example
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April 21, 2019

16. FLOSCAN Objectives FLSCAN: It is a density-based algorithm Objectives:
FLOSCAN as a pre-clustering step to a clustering algorithm: Data points, sharing some common features, will be closer to each other. This will enhance the shape of potential clusters and therefore improve the efficiency of a clustering algorithm.
FLOSCAN can also be used as either a clustering algorithm or a classification algorithm: capable of discovering clusters of different shapes and detecting noise points.
A. Bellaachia
April 21, 2019

17. FLOSCAN Pseudo Code: Initialization of the above parameters.
Calculate the distance between each document di
For each iteration do
For each document di do
Find the neighbors, N, of di using the minimum and maximum distances.
For each neighbor dj in N do
Calculate the align vector and attract vector of di using dj
Calculate the new direction vector for di
End do
Calculate the new direction vector of di.
End for.
A. Bellaachia
April 21, 2019

18. Experimental results
To measure the performance of FLOSCAN, we use the LDC TDT Corpus.
We have randomly chosen about 1,200 stories:
about half collected from Reuters newswire and
half from CNN broadcast news transcripts.
Twenty-five topics were defined in the original release.
A. Bellaachia
April 21, 2019

19. Document Representation
Use vector model to represent documents in the collection.
Remove stopwords
Assign a weight to each term in each document: Augmented weight
L(tji) = * (tf(tji)/tf(max))
where, tf(max) = max{ tf(t1i), tf(t2i), ... , tf(tmi)} and m is the max number of terms in the collection.
A. Bellaachia
April 21, 2019

20. Evaluations F-Measure: A combination of IR precision and recall.
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April 21, 2019

21. Evaluations Centroid Similarity (CS):
It computes the similarity between the centroids of all clusters. Given a set of k clusters, CS is defined as follows:
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April 21, 2019

22. Experimental Results
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April 21, 2019

23. Experimental Results
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April 21, 2019

24. Experimental Results
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April 21, 2019

25. Experimental Results
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April 21, 2019

26. Experimental Results
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April 21, 2019

27. Conclusion & Future Work
FLOSCAN: Introduce a new flocking based algorithm that can be used:
Clustering
Pre-preprocessing step in a data mining algorithm.
Experimental results and comparison to DBSCAN.
Future Work include:
Other Experiments with large datasets and other artificial-life algorithms such as Ant algorithm.
Analyze the scalability of FLOSCAN
Use FLOSCAN as a classification algorithm
Theoretical analysis of the initial parameters required by FLOSCAN, namely maximum distance, number of iterations, speed value.
A. Bellaachia
April 21, 2019

28. Questions
.. Thank you…
A. Bellaachia
April 21, 2019