The document presents a new algorithm designed for mining flexible periodic patterns in time series databases, supervised by Dr. Chowdhury Farhan Ahmed. It addresses limitations of existing algorithms by utilizing a suffix tree-like structure to efficiently generate patterns with variable starting positions and multiple periodicity types in a single run. Experimental results indicate improved performance and reduced memory consumption over traditional methods.

1. 1 I NAME OF PRESENTER
An Efficient Approach to Mine Flexible
Periodic Patterns in
Time Series Databases
Supervised by
Dr. Chowdhury Farhan Ahmed
Associate Professor
Md. Samiullah (Lecturer)
Presented by
Ashis Kumar Chanda
Swapnil Saha
Department of Computer Science and Engineering
University of Dhaka

2. 2 I NAME OF PRESENTERCSE, DU2
Introduction
Problem Definitions
Motivation
Contribution
Experimental Results
Conclusion
1
2
4
7
8
Existing Algorithms3
The Proposed Algorithm
5
Topics to be covered
6

3. 3 I NAME OF PRESENTERCSE, DU3
Extracting hidden patterns or structure
Gain Information from huge data
Data Mining
Introduction
Example:
Periodic amount of money withdrawn within a fixed time
Interval from an ATM booth in a specific location
Day Time slot Money amount (million)
Sun 12 am - 8 am
8 am – 4 pm
4 pm – 12 am
2
6
9
Mon 12 am - 8 am
8 am – 4 pm
4 pm – 12 am
1.2
12
9
Thu 12 am - 8 am
8 am – 4 pm
4 pm – 12 am
1.5
3
4.5

4. 4 I NAME OF PRESENTERCSE, DU4
Flexible Periodic Pattern:
Skipping a single or couple of particular intermediate
characters or events which are not interesting in the user's
point of view
F = ‘abc’ or ‘adc’
Introduction (cont.)
Example:
Consider T = {abc adc abc}
Flexible pattern = ‘a*c’
Where ‘*’ indicates any unimportant intermediate events
‘a*c’

5. 5 I NAME OF PRESENTER
Problem Definition
CSE, DU5
Flexible Pattern Mining:
Given a sequence with n number of characters or events,
S = {e1, e2, e3 ... en} a time series database, user specified
maximum event skipping threshold, ϴ and support threshold, σ
Mine all possible Flexible Periodic sequence of events,
FP = {e1, e2, e3 ... ei} Є S
that satisfy σ, and considering variable starting position st,
where i ≤ n with maximum ϴ number of unimportant
intermediate events

6. 6 I NAME OF PRESENTER
Existing Algorithms
CSE, DU6
Effective
periodic
pattern
mining
Apriori based
sequential
pattern mining
Nishi et al. 2013 Huge candidate
set,
False pattern
generation
Most notable algorithms:Algorithm Mechanism Authors Year Drawbacks
CONV Convolution
process
M. G. Elfeky et
al.
2005 Fails in insertion,
deletion process
WARP Time warping
technique
M. G. Elfeky et
al.
2005 Only detects
segment
periodicity
STNR Suffix tree Rasheed et al. 2010 Lack of skipping
intermediate
events

7. 7 I NAME OF PRESENTER
Motivation
CSE, DU7
Apriori based approach should be avoided
To vary starting positions in generated sequences
Mine three types of periodicity detection in one run

8. 8 I NAME OF PRESENTER
Contribution
CSE, DU8
Reduced redundant patterns>
Developed a new algorithm using suffix tree like data
structure to generate Flexible Periodic Patterns
>
Also proposed a new periodicity detection algorithm>
Capable of mining all three types of periodicity in a single run>
Considered variable starting positions from the given time
series sequence
>

9. 9 I NAME OF PRESENTER
Terms & Definitions
CSE, DU9
T = {acbd afbd agbd}
Occurrence vector:
• occ_vec[a] = [0, 4, 8]
• occ_vec[c] = [1]
• occ_vec[b] = [2, 6, 10]
Confidence of ‘a’:
• actual periodicity = 3
• perfect periodicity = 3
• Confidence = 3 / 3
Confidence of ‘c’:
• actual periodicity = 1
• perfect periodicity = 3
• Confidence = 1 / 3
Perfect periodicity = (endpos – stpos + 1)/
period
Confidence = actual periodicity/ perfect
periodicity
0 1 2 3 4 5 6 7 8 9 10 11

10. 10 I NAME OF PRESENTER
Terms & Definitions
Ladder factor:
• lad_fact[A2] = 3
• lad_fact[A6] = 2
CSE, DU10
a
$ $
$
b
bb
b
A5
A4
A3
A2
A1
A6
A7
A8
A9
Fig: SSES tree for T = {abb$}
Length vector:
• len_vec[A2] = [3]
• len_vec[A6] = [2, 1]
support threshold, σ = 50%
lad_fact = nth max(len_vec)
n = size of len_vec * σ

11. 11 I NAME OF PRESENTER
The Proposed Algorithm
CSE, DU11
Key Features:
- Apply discretization technique on given database
- Construct the Single Symbol Edge based Suffix (SSES)
tree
- Calculate occurrence vector at the time of construction
- Traverse the tree level-wise
- Mine patterns following joining property
- Check each generated patterns through the proposed
periodicity detection algorithm

12. 12 I NAME OF PRESENTER
SSES Tree Construction
12
1
T = { }abcabbabb$
12 45
3934
2
3
4
5
6
7
8
9
10
11
13
14
15
16
17
18
19
20
29
30
31
32
33
43 35 44
36
37
38
40
41
42
21
22
23
24
25
26
27
28
a
a
a
a
a
a
a
a
a
a
$
$
$
$
$
$
$
$
$
bb
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
c c
c1
5 17
1412
2
3
4
6
711
13 16 15
8
9
10
a
a
a
a
a
$
$
bb
b b
b
b
c c
c
Period = 3
root

13. 13 I NAME OF PRESENTERCSE, DU13
Unique event occ_vec
Occurrence vector calculation
b [1, 4, 5, 7, 8]
Confidence calculation
Pattern occ_vec confidence status
b [1, 4, 7] 100% √
Algorithm Demonstration
1
5 17
1412
2
3
4
6
711
13 16 15
8
9
10
a
a
a
a
a
$
$
bb
b b
b
b
c c
c
Patterns
Pattern occ_vec
b [1, 4, 5, 7, 8]
L1
L4 L7 L5
L8
σ = 50%

14. 14 I NAME OF PRESENTER
bb [4, 7]
CSE, DU14
Unique event occ_vec
Occurrence vector calculation
c [1]
Confidence calculation
Pattern occ_vec confidence status
bc [1] 33% χ
1
5 17
1412
6
7
13 16 15
10
a
a
a
$
$
b
b
b
c
ba [5] 100% √
bb [4, 7] 100% √
b [4, 7]
a [5]
Patterns
Pattern occ_vec
b [1, 4, 5, 7, 8]
Join
ba [5]
b* [1, 4, 5, 7]
Algorithm Demonstration
L1
L4 L7 L5
L8
σ = 50%

15. 15 I NAME OF PRESENTERCSE, DU15
Unique event occ_vec
Occurrence vector calculation
a [1]
Pattern occ_vec confidence status
bba [4] 50% √
1
5 17
1412
6
7
13 16 15
10
a
a
a
$
$
b
b
b
c
b*a [1, 4] 66% √
baa [] 0% χ
a [1, 4]
b [5]
Patterns
Pattern occ_vec
Join
ba [5]
b* [1, 4, 5, 7]
bab [5] 100% √
bbb [] 0% χ
b*b [5] 100% √
bb [4, 7]
b*a [1, 4]
bab [5]
bba [4]
b*b [5]
Algorithm Demonstration
Confidence calculation
L1
L4 L7 L5
L8
σ = 50%

16. 16 I NAME OF PRESENTER
Final Result
CSE, DU16
Mined patternsPattern occ_vec
a [0, 3, 6]
ab [0, 3, 6]
abb [3, 6]
a*b [3, 6]
b [1, 4, 7]
bb [4, 7]
ba [5]
b*a [1, 4]
bab [5]
b*b [5]
c [2]
ca [2]
cab [2]
c*b [2]
bba [4]
A1
1
5 17
1412
2
3
4
6
711
13 16 15
8
9
10
a
a
a
a
a
a
$
$
bb
b b
b
b
c c
c
Pattern occ_vec
Pattern occ_vec
T = {abcabbabb$}

17. 17 I NAME OF PRESENTER
Experimental Result
CSE, DU17

18. 18 I NAME OF PRESENTER
Conclusion
CSE, DU18
Future Works:
Improve the proposed procedure to compare with noise-
resilient features
Develop an efficient way to execute in parallel time series
databases
Reduce memory consumption
Summary:
 Mine Flexible Periodic Patterns using Suffix tree like
structure
 Improve performance by pruning tree
 Consider variable starting positions in given time sequence

19. 19 I NAME OF PRESENTER
References
CSE, DU19
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20. 20 I NAME OF PRESENTERCSE, DU20
Questions?

21. 21 I NAME OF PRESENTERCSE, DU21
Thank You