The study on mining temporal patterns and related applications in dynamic social network(20120928晚)
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The study on mining temporal patterns and related applications in dynamic social network(20120928晚)
- 1. Mining Temporal Pattern and Related Applications Yi-Cheng Chen 陳以錚 1
- 2. Curriculum Vitae Basic Information Birthday – Aug. 31, 1978 Education Depart. of CSE, YZU (B. S. 2000) Depart. of CS, NTUST (M. S. 2002) Depart. of CSIE, NCTU (Ph. D. 2012) Advisor: Prof. Suh-Yin Lee ( 李素瑛 教授 ), Wen-Chih Peng ( 彭文志 教授 ) Ph. D. Dissertation: A Study on Time Interval-based Sequential Patterns Mining 2
- 3. Outline Current Research Temporal Pattern Mining Social Network Analysis Smart Home Application Cloud Computing 3
- 4. Why Data Mining? Commercial Viewpoint Lots of data is being collected Web data, e-commerce purchases at department Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong Provide better, customized services for an edge (e.g. in Customer Relationship Management) 4
- 5. Why Data Mining? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite telescopes scanning the skies microarrays generating gene expression data scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists in classifying and analyzing data in Hypothesis Formation 5
- 6. Data Mining We are buried in data, but looking for knowledge Data mining Knowledge discovery in databases Extraction of interesting knowledge (rules, regularities, patterns) from data in large databases 6
- 8. Sequential Pattern Mining Point-based sequential pattern mining Customer analysis, network intrusion detection, finding tandem repeats in DNA sequence… Simple relation between point time point-based beer milk diaper beer milk diaper Three relation (before, equal, after ) with min_sup = 2, 〈(ab)dc〉 is a frequent sequential pattern 8
- 9. Interval Data Everywhere !! Interval data Data has duration time Clinical data, library data, appliance usage data Applications Diagnosis System, recommendation system, Smart home DB Diagnosis System Recommendation Smart Home 9
- 10. Temporal Pattern Mining Interval-based sequential pattern mining Library reader analysis, patient disease analysis, stock fluctuation, ... Complex relations time interval-based cough Chess pain fever Allen’s 13 temporal relations With min_sup = 4, is a frequent temporal pattern 10
- 11. Allen Relationship Allen’s 13 temporal logics describe relationship between any two events (binary relation) [ACM 1983] 11
- 12. Real example Some temporal patterns generated from NCTU library 12
- 13. Motivation Representation Allen’s relations are binary relation Express the relation more than 3 intervals Efficient algorithms Ambiguous problem Space usage Mining temporal pattern * Mining closed temporal pattern Incrementally maintain discovered temporal pattern and closed temporal pattern Related applications Social network Smart home 13
- 14. Proposed Method Coincidence representation Segment intervals into disjoint slices Nonambiguous and compact representation Endpoint representation Global information of a sequence Nonambiguous and compact representation TPMiner (Temporal Pattern Miner) Pattern-growth approach Without candidate generation and test Two components RPrefixSpan Pruning strategies 14
- 15. Coincidence representation Segment intervals into disjoint slices Four kinds of event slice Start slice (+), intermediate slice (*), finish slice (-) and intact slice ( ) Coincidence Slices occurring simultaneously Space usage (for a k-pattern) Best: k, Worst: 2k space A event intervals coincidence B (A+) (A−B+) (B−) coincidence representation: C D E (C+) (C*D) (C−) (E) (A+) (A−B+) (B−) (C+) (C*D ) (C−) (E) 15
- 16. Incision strategy A data structure, endtime_list Sort and merge (A, 1, 4) Trace endtime_list one-by-one (B, 2, 5) (C, 2, 8) endtime_list symbol time type 1 4 2 5 2 8 3 5 5 7 s f s f s f s f s f endtime_list symbol time type sort merge A BC D A BD E E C 1 2 3 4 5 5 7 8 s s s f f s f f (E, 5, 7) coincidence representation: (A+) (B+ C+) (A− D+ ) (B− D− ) @ (E) (C− ) … A A B B C C D D E E (D, 3, 5) trace one- by- one 16
- 17. Endpoint Representation time points of events A B C D A+ ( B+ C+ ) A− ( B− C− D+ ) D− Sequence of ordered time points +: start time, − : finish time Nonambiguous Space usage (for a k-pattern) 2k space 17
- 19. TPMiner – RPrefixSpan (1/2) Every item is disjoint The relations among slices are simple Before, equal and after (like time-point data) RPrefixSpan Borrow the idea of PrefixSpan Scan local database to find frequent slices Append and extend the pattern Project database Pruning strategy Reduce search space Pre-pruning and post-pruning 19
- 20. TPMiner – RPrefixSpan (2/2) .. D |e1... D |e2 … scan database frequent items: e1, e2, ..., ei, ..., en … D |ei D |en transform sequences and project database .. .. D |ei... .. .. collect all mining patterns Frequent temporal patterns .. D |en... .. .. .. D |e2... … D … D |e1 .. .. recursively project database and append & extend pattern 20
- 21. Pruning Strategy – Pre-pruning Utilize the concept of slice and coincidence Start slices and finish slices occur in pairs Only require projecting the frequent finish slices which have the corresponding start slices in their prefixes Non-qualified pattern 〈 A+ A− 〉 D|〈A+ A−〉 〈A B 〉 + … 〈 A+ 〉 D| 〈A+〉 scan database frequent local slice : A− , B+, B− , C + D|〈A+ B+〉 Non-promising projection can be pre-pruning ! 〈 A+ B− 〉 D|〈A+ B−〉 〈 A+ C 〉 D|〈A+ C〉 21
- 22. Pruning Strategy – Post-pruning Utilize the concept of slice and coincidence Start slice always appear before finish slice Only collect the significant postfixes With respect to a prefix α, all finish slices in postfix have corresponding start slices in α … ... S1: 〈(B ＋ )(D ＋ )(E)(D － )(B － )〉 S2: 〈(B ＋ )(B － D ＋ )(E)(D － )〉 S3: 〈(B)(A)(D ＋ )(E)(D － )〉 〈 E〉 ... A coincidence database D Projected database can be post-pruning D |〈 E 〉 S1: 〈(D － )(B － )〉 S2: 〈(D － )〉 S3: 〈(D － )〉 … Insignificant sequences 22
- 23. Experimental Results (1/2) D200k – C40 – N10k 70000 IEMiner TPMiner-CR TPMiner-ER 40000 30000 20000 10000 3500 3000 2500 2000 1500 1000 500 0 0 1 0.9 0.8 0.7 0.6 0.5 minimum support (%) (a) The performance of six algorithms 1 0.9 0.8 0.7 0.6 0.5 minimum support (%) (b) The number of temporal patterns N10k – C20 – N10k 2500 H-DFS ARMADA 2000 memory usage (MB) execution time (sec) 50000 number of generated patterns H-DFS ARMADA TPrefixSpan 60000 D200k – C40 – N10k 4000 TPrefixSpan IEMiner 1500 TPMiner-CR TPMiner-ER 1000 500 0 1 0.9 0.8 0.7 0.6 0.5 minimum support (%) 23
- 24. Experimental Results (2/2) 7000 6000 TPMiner-CR 5000 TPMiner-CR without pre-pruning strategy TPMiner-CR 5000 execution time (sec) execution time (sec) 6000 4000 3000 2000 TPMiner-CR without post-pruning strategy 4000 3000 2000 1000 1000 0 0 1 0.9 0.8 0.7 0.6 1 0.5 minimum support (%) 0.8 0.7 0.6 0.5 minimum support (%) (a) The performance test of influence on pre-pruning strategies (b) The performance test of influence on post-pruning strategies 6000 8000 4000 7000 TPMiner-CR TPMiner-CR without subset-pruning strategy 6000 TPMiner-CR without any pruning strategy execution time (sec) TPMiner-CR 5000 execution time (sec) 0.9 3000 2000 1000 5000 4000 3000 2000 1000 0 0 1 0.9 0.8 0.7 0.6 0.5 minimum support (%) (c) The performance test of influence on subset-pruning strategies 1 0.9 0.8 0.7 0.6 0.5 minimum support (%) (b) The performance test of influence on all proposed pruning strategies 24
- 26. Smart Home Application Home Home Server light Air Conditioner Cloud Database Current Behavior (1) Sensor data log P1: P2: P3: (3) Behavior Detection Alarm D-Link controler light ID 2 Air Conditioner … Usage Patterns (2) Pattern Mining ID 2 Usage Pattern Light ID 3 ID 4 on off on off on off on off light Air Conditioner Current Behavior (4) Abnormal Detection Remote Control (5) System Alarm & Remote Control 26
- 27. Dynamic Social Network (1/2) Dynamic social network A sequence of interaction graph … Nodes and edges vary with time A lossless transformation Graph sequence interval sequence t1 SID A A A E B E B B D C D C G2 start time finish time B D E 1 2 4 3 4 6 A C 1 1 3 3 B D E 1 2 4 3 4 6 A C 1 1 3 3 E B D C G1 E event symbol A A D …. B C G3 G4 C D t2 t3 event sequence B E D A C B E D A C 27
- 28. Dynamic Social Network (2/2) Reduce the complexity of graph Avoid isomorphism testing Dynamic Social Network Analysis Pattern mining Classification Recommending system Network sampling Clustering 28
- 30. Social Network Analysis A graph representation Nodes and edges 30
- 32. Advertisement Budget According to , advertisement spending on worldwide social networking sites 2008, $23.3 millions 2010, $23.6 billions 2011, almost $25.5 billions Advertisement spending 32
- 33. Influence Maximization Word-of-mouth effect in social network Influence maximization problem Select initial users (seeds) so that the number of users that adopt the product or innovation is maximized social network social network Seeds select 33
- 34. Motivation Characteristic of social network Community structure 10 5 4 6 1 9 7 2 11 3 10 5 12 4 8 6 1 9 7 2 11 3 12 8 Community and degree heuristic (CDH) Utilize community information Avoid influence overlapping 34
- 35. Proposed Algorithm – CDH Framework of CDH 35
- 36. CDH – Adjust Step Adjust selected fundamental nodes Seeds selected from large community may activate more inactive nodes than small community Replace the fundamental node in small community If we can activate more inactive nodes Finally, output the result as selected seed nodes second largest degree node in C1 replace!! largest degree node in Ck C1 C2 C3 …… Ck delete!! 36
- 37. Experimental Results - Facebook 37
- 39. Recommendation System predict the ratings or preferences using a model build from the characteristics (a) amazon.com (b) youtube.com 39
- 40. Collaborative Filtering (CF) Calculate the similarity between the active user and the other users • Person’s correlation, cosine similarity, conditional probability, etc. 2. Predict the rating of items that have not been rated by the active user 3. Output the top-k items by the predicting results 1. 40
- 41. item i1 i2 i3 4 1 i4 4 user A B 2 C 3 Avg. Of user 3 4 3 3 2 2 (1 − 3)(2 − 3) wa , b = normalize (1 − 3)(3 − 2) + (4 − 3)(3 − 2) wa , c = normalize (4 − 3) * wa , b + (2 − 2) * wa , c pa , i = 3 + normalize 4 41
- 42. Motivation Dynamic! Dynamic! Dynamic! Why we need dynamic All things vary with time Dynamic Collaborative Filtering consider the time influence in the calculation. Without considering about the time the results of prediction might be out of date. 42
- 43. Dynamic Similarity based on Collaborative Filtering (DSCF) ( user->item : rating (time) ) 1 -> 1193 :5 (2012.5.18) 5 -> 661 :3 (2012.3.5) 3 -> 914 :3 (2012.6.27) 1 -> 3408 :4 (2012.3.18) …… ( user->item : rating (time) ) 9 -> 6610 : 5 (2012.7.8) 2 -> 6610 : 3 (2012.7.15) … …. Msimt0−1 DBt0−1 ………. ….. .. *α Msimt0 ………. …. .. Msimtt −1 DBtt −1 ………. …. .. Msimt0 = α ⋅ Msimt0−1 + (1 − α ) ⋅ Msimtt −1 *(1-α) *(1-α) 43
- 44. Advanced DSCF α (similarity decay value, SDV) might not be consistent for all time. each user might have his/her own SDV in different time points. feedback predicted values from actual values 44
- 45. ? Active user Predict pa ,i = ra ∑ + k j =1 (rj ,i − rj ) ⋅ [α ⋅ sim′ , j + (1 − α ) sim′′, j ] a a ∑ k j =1 ′ [α ⋅ sim′ , j + (1 − α ) sima′, j ] a Recommend Aa,i Active user ∑ + k Feedback Aa ,i = ra j =1 ′ ′ ( r j ,i − rj ) ⋅[α ⋅ sima , j + (1 −α ) sima′, j ] ∑ k j =1 ′ [α ⋅ sima , j + (1 −α ) sim′′, j ] a 45
- 47. 47
Editor's Notes
- Here is Allen’s 13 temporal relation. That is …… For two intervals, we can describe their relationship by their start times and finish times. For example, interval A meets interval B, if A’ finish time is equal to B’s start time.
- By our observation, there are three important issues for mining closed temporal patterns. Complex relationship. Since an interval has duration time, the relation between any two intervals is complicated, Representation. Since all Allen’s temporal relations are binary relations. That means it can describe the relation between any two intervals easily. However, when we want to express the relation more than 3 intervals, here comes the problem. How can we express a pattern non-ambiguously? And what is the space usage to describe a pattern correctly? The last issue is how to design an efficient algorithm? Could we avoid candidate generation? And how can we reduce the number of scanning database? In this paper, we propose a non-ambiguous and compact representation to express a temporal pattern. And also propose an efficient method that can simplify the process of complex relation, And avoid candidate generation.
- By our observation, there are three important issues for mining closed temporal patterns. Complex relationship. Since an interval has duration time, the relation between any two intervals is complicated, Representation. Since all Allen’s temporal relations are binary relations. That means it can describe the relation between any two intervals easily. However, when we want to express the relation more than 3 intervals, here comes the problem. How can we express a pattern non-ambiguously? And what is the space usage to describe a pattern correctly? The last issue is how to design an efficient algorithm? Could we avoid candidate generation? And how can we reduce the number of scanning database? In this paper, we propose a non-ambiguous and compact representation to express a temporal pattern. And also propose an efficient method that can simplify the process of complex relation, And avoid candidate generation.
- We propose a new representation named coincidence representation. We use the global information of a sequence to segment intervals into disjoint slices It’s a nonambiguous and compact representation. Based on coincidence representation, we propose a CTMiner algorithm. It’s a pattern-growth approach. And do not need candidate generation and test. CTMiner can be decomposed into two components, Incision strategy and CprefixSpan. Incision strategy is used to transform sequence into coincidence representation. CprefixSpan is used to mine all frequent temporal patterns.
- Coincidence representation segments intervals into disjoint slices, according to the arrangement of all end time points in the sequence. There are four kinds of event slices: Start slice, like this is the start slice of A, start slice of B, Start slice of C. Intermediate slice, like this is the intermediate slice of C. Finish slice, like this is the finish slice of A, finish slice of B, finish slice of C. Intact slice, means do not be cut. Like, intact slice of D, intact slice of E. Coincidence is the group of event slices which occurs simultaneously. So these are coincidences. Concatenate all coincidences together can form the coincidence representation of a sequence.
- CTMiner has two main components Incision strategy and CprefixSpan. Incision strategy is used to transform sequence into coincidence representation. We first put all end time points of all intervals into endtime_list. Then sort by time in increasing order. And merge two symbols if time and type are the same. Like B’s start time and C’s start time. Then trace endtime_list one-by-one can transform sequence into coincidence representation. Like this example.
- Temporal representation It use the sequence of order time point to express a temporal pattern. plus represent the start time and minus represent the finish time. Like this example, pattern ABCD is expressed as this sequence. The start time of A is smaller than the start time of B, so here is A smaller then B. The start time of B is equal to the start time of C, so here is B equal C. Temporal representation has no ambiguous problem. It uses 4k 1 space to describe a k-pattern. ( 2k for event index, and 2k minus 1 for relation describer) TprefixSpan adopts this representation
- Mining closed pattern actually require a complicated process. It usually need a lot of closure checking. That is checking whether the mining pattern is a sub-pattern of existed pattern, Or the previous mining pattern is a sub-pattern of current mining pattern. We propose an endpoint representation. Which can transform quickly from interval data. Based on this simple representation, we can do the closure checking easily. It also is a nonambiguous and compact representation. So we can transform the example database to a endpoint database.
- Every coincidence in the coincidence sequence is disjoint. So the relations among slices are simple, just before, equal and after. We borrow the idea of PrefixSpan An efficient algorithm for time-point data. CprefixSpan has three step: Scan local database to find frequent slices Append and extend the pattern Project database By utilizing the concept of slice and coincidence, We propose two pruning strategies to reduce search space. Pre-pruning and post-pruning.
- This is the overview of CprefixSpan. We first scan database to get all frequent intervals. And transform sequences and project database by these frequent intervals. Then project database and append & extend pattern recursively. Finally, collect all mining results can get all frequent temporal patterns.
- Pre-pruning strategy is based on the concept of slice and coincidence. Since the start slices and finish slices always occur in pairs, We only require projecting the frequent finish slices which have the corresponding start slices in their prefixes. Like this example, for the projected database of A plus, After scanning database, suppose we have four frequent slices, A, B+, B and C. Then we append and extend pattern and build four projected database. However, B minus do not have corresponding B plus in prefix pattern. So by pre-pruning strategy, we can avoid a lot of non-promising projections.
- Post-pruning strategy is based on the concept of slice and coincidence. Since the start slices and finish slices always occur in pairs, And Start slice always appear before finish slice. So when we building a projected database, We only collect the significant postfixes. significant postfixes means with respect to a prefix , all finish slices in postfix have corresponding start slices in . For example, given a coincidence database, When we building the projected database with E, All three sequences are insignificant. So, the projected database with E can be post-pruning.
- Mining closed pattern actually require a complicated process. It usually need a lot of closure checking. That is checking whether the mining pattern is a sub-pattern of existed pattern, Or the previous mining pattern is a sub-pattern of current mining pattern. We propose an endpoint representation. Which can transform quickly from interval data. Based on this simple representation, we can do the closure checking easily. It also is a nonambiguous and compact representation. So we can transform the example database to a endpoint database.
- Mining closed pattern actually require a complicated process. It usually need a lot of closure checking. That is checking whether the mining pattern is a sub-pattern of existed pattern, Or the previous mining pattern is a sub-pattern of current mining pattern. We propose an endpoint representation. Which can transform quickly from interval data. Based on this simple representation, we can do the closure checking easily. It also is a nonambiguous and compact representation. So we can transform the example database to a endpoint database.
- Based on endpoint representation, we propose a CEMiner algorithm. It’s a pattern-growth approach. And do not need candidate generation and test. CEMiner can be decomposed into two components, Closure checking and Pruning Strategy.
- Sequential pattern mining is an important and basic research in data mining domain because it is useful in many applications. Such as, customer’s analysis, network intrusion detection, and finding tandem repeats in DNA sequences, to name a few. Like this example. We can find some behaviors and common patterns from customer’s buying record, like: buy milk than buy (beer & diaper) together. However, in some real world applications, an event is usually a time interval instead of a time point. It usually has a duration time. Like, library reader analysis, patient disease analysis, stock fluctuation, to name a few. Like this example. We can find the correlation between diseases from patient’s record. The relation between time points is simple, just before, equal and after. But the relation between time intervals is quite different. It is more complicated. We usually use Allen’s 13 temporal relation to describe the relationship between any two time intervals .
- According to eMarketer, we can know that advertisement spending on social networking grow rapidly every year. Look at this figure, In 2008, the total expense was about $23.3 billions. In 2010, it was about $23.6 billions. But in 2011, it almost reached $26 billions. So, obviously, the research and related technology on social network marketing is very very important
- According to eMarketer, we can know that advertisement spending on social networking grow rapidly every year. Look at this figure, In 2008, the total expense was about $23.3 billions. In 2010, it was about $23.6 billions. But in 2011, it almost reached $26 billions. So, obviously, the research and related technology on social network marketing is very very important
- This is the framework of CDH algorithm Given a social network represented as a graph, we first detect community. Then use community information to construct the potential pool and find the fundamental nodes from the pool. Finally, adjust the fundamental nodes to select the seed nodes.