FICC2019

Towards Enhancing Historical Analogy:
Clustering Users Having Different Aspects of Events

Ryohei IKEJIRI（Assistant Professor, The University of Tokyo, Japan）
Ryo YOSHIKAWA（Assistant Professor, Nagoya Bunri University, Japan）
Yasunobu SUMIKAWA（Assistant Professor, Tokyo University of Science, Japan）

Background■Historical analogy
・HA =Applying historical causation to solve modern social issues

・Learning HA is eﬀective in solving modern social issues(Staley 2002)

・Learning HA is regarded as important in history education

Protective tradein France in 17C grew
national industries.
So, protective trade
should be promoted.

News about
ModernProtective Trade

Background■Towards Enhancing Historical analogy
・Careful discussion is needed for using HA (Fischer 1970)
・Checking the validity of historical analogy in a pair who havesame aspects is enhancing historical analogy (Ikejiri 2011)
・Group discussion between two pairs who have (Ikejiri et al. 2016)
diﬀerent aspects is enhancing historical analogy (Holyoak 1980)①
Protective trade
in France in 17C grew
national industries.
So, protective trade
should be promoted②

NewsaboutModernProtectiveTrade③④

Bloc Economy
in 19C caused
World War Ⅱ.
So, protective trade
should be stopped

Purpose・We propose a novel clustering algorithm to promotehistorical analogy through group discussions
・Our algorithm has two objectives to create groups:
1) ﬁnding two users(=pair) who focus on
the same aspects of an event2) aggregating two pairs (=group) that havediﬀerent aspects in the same event

・・・・Related Works
・Traditional clustering algorithm basicallymakes groups by similar data
→The key contribution of our algorithm is tocombine not similar data into a group
・Below clustering algorithms are close to ours
ー Partitioning-based algorithm (ex. k-means)
ー Hierarchy-based algorithm(ex. Birch)
ー Distribution-based algorithm (ex. GMM)
ー Graph-based algorithm(ex. Spectral)Data Collection
・We have developed a educational system forpromoting HA with which each user searches forhistorical events similar to the selected news
(Ikejiri et al. 2016)

Data Collection
・For searching similar history, we use 13 categories
that characterize both modern society and historicalevents on deﬁnition of Encyclopedia of Historiography

In Politics Field

In Economy Field

1. reign
2. diplomacy
3. war

4. Production
5. Commerce

In Culture Field

In Society Field

6. study
7. religion
8. literature and thought
9. technology

10. popular movement
11. community
12. disparity
13. environment

Data Collection
・We regard the categories selected by each user as
reﬂecting each aspects of historical analogy

Environment, Study, TechnologyMethodology■Overview of our algorithm①②③

①Feature Vector Creation
・We take event categories selected by users
・We convert the categories to a feature vector whoseelements are represented by 0 or 1・We create a feature vector for each user

student

reign

diplo
macy

war

produ
ction

comme
rce

study

religionliterature &thought

technology

popularmovement

community

disparity

environment

Student 1

0

100

100000000

Student 2

100000

100

10000

100

10

1000

0

00…

student N

②Creating Pairs
A) Measuring similarity between two users
・Pair Similarity is measured by counting how many
common categories between two users
・All Pair Similarities are calculated
student

reign

diplo
macy

war

produ
ction

commerce

study

religionliterature &thought

technology

popularmovement

community

disparity

environment

Student 10100

100000000

Student 2

100000

100

10000

100

10

1000000…

student N

②Creating Pairs
B) Creating a Set of Pairs
・We solve the maximum problem that is essentially Knapsack problem
・Our algorithm ﬁnd and ﬁx a pair with max Pair Similarity
・The same processing is repeated with the remaining students

S1 S2

S2 S3

Sim:1

Sim:4
S1 S4

S1 S5

Sim:2

Sim:3

③Creating Groups
A) Measuring Similarity between two pairs
・A feature vector for a pair is created considering pairlevel selected categories
・Group Similarity is measured by counting how many
common categories between two pairs
・All Group Similarities are calculated
pair

reign

diplomacy

war

produ
ction

comme
rce

study

religion

literature &thought

technology

popularmovement

commun
ity

dispa
rity

environment

pair 10200200000000pair 210000020010000200202000000…
pair N

③Creating GroupsB) Creating a Set of Groups
・We solve the minimum problem that is essentially Knapsack problem
・Our algorithm ﬁnd and ﬁx a group with minimum Group Similarity
・The same processing is repeated with the remaining pairs

P1 P2

P2

Sim:0
P3

P4

Sim:0

P3

P5

Sim:4

P3

Sim:8

Experimental Evaluation■Setup・One present event is prepared (Labor Problem)
・40 high school students participated in this experiment→Each students selected some categories for using HA■Baselines
・We compared our algorithm with K-means, Birch, GMM,Spectral（We set 10 clusters as a result of dividing thenumber of users by 4 in all algorithms）

Experimental Evaluation & Result①Size of clusters→kmeans, Birch, and GMM algorithms fail to include
more than 2 users in a few clusters

Experimental Evaluation & Result②Quality of Clustering（= We use Calinski and Harabaz score）
ーAverage Minimum distances in a clusterーInner-cluster（=Average sum of distances of all
combinations in each cluster）

Conclusion■Conclusion
・Our clustering algorithm makes groups by combining
not only similar users but also not similar pairs
・Experimental results proved that only our algorithm
creates suitable groups■Future Works・analyzing how well users can discuss with our algorithm
・proposing more sophisticated grouping algorithm
・analyzing robustness of our algorithm