Creative Data Mining : Documentation of the teaching results from the Spring Semester 2017

similarity in general. Take checkpoint 11 for example, it’s clear that chords by two sides are much wider
than starting chords and in ending the middle. points which is the furthest pair although they were placed close to each
other. Similarly, 1 and 13, 2 and 14 are also very far away. Then it shows a tendency that
checkpoints closer to each other give out a higher similarity in general. Take checkpoint 11 for
starting and ending points which is the furthest pair although they were placed close to each
example, it’s clear that chords by two sides are much wider than chords in the middle.
starting other. Similarly, and ending 1 and points 13, which 2 and is 14 the are furthest also very pair far although away. Then they were it shows placed a tendency close to each that
other. checkpoints Similarly, closer 1 to and each 13, other 2 and give 14 out are a also higher very similarity far away. in Then general. it shows Take checkpoint a tendency 11 that for
checkpoints example, it’s closer clear that to each chords other by two give sides out a are higher much similarity wider than in general. chords in Take the middle. checkpoint 11 for
example, it’s clear that chords by two sides are much wider than chords in the middle.
Figure 2: Similarity of subjective feelings
Following the Moran’s I index measuring two-dimensional autocorrelation, we also calculated
Following the overall the autocorrelation Moran’s I index for measuring the Figure similarity 2: Similarity two-dimensional metric of subjective using following autocorrelation, feelings formula: we also calculated the overall
autocorrelation for the similarity Figure metric 2: using Similarity following of subjective feelings
Following the Moran’s I index measuring 14 two-dimensional D formula:
∑
ij autocorrelation, we also calculated
j=1 C ij ∗ exp⁡(− ⁡)
Following the overall the autocorrelation Moran’s I index for the measuring similarity two-dimensional metric using ∑ j D ij following autocorrelation, formula: we also calculated
I
the overall autocorrelation for the i =
similarity ∑14
⁡⁡, j ≠ i
j=1
metric C ij /13using following formula:
14
D
∑
ij
j=1 C ij ∗ exp⁡(−
14
D
⁡)
∑ D
Cij is the similar metric and Dij is the ∑walking ij
j=1 C ij ∗
distance.
exp⁡(− j It actually ij
⁡)
uses distance-weighted average
I i =
∑14
∑ j D
⁡⁡, j ≠ i
of similarity metric divided by ij
I
arithmetic
i =
average. j=1 C ij /13 When similar
∑14
⁡⁡, j ≠
metric
i
is evenly distributed, the
Ii would equal 1. Ii larger than 1 illustrates j=1that C ij /13 in general closer points have higher similar
Cij is the similar metric and Dij is the walking distance. It actually uses distance-weighted average
metric. The result shows that for most checkpoints it confirms our assumption but only check
Cij Cij of is is similarity the similar metric metric divided and by Dij is arithmetic is the the walking average. distance. When It actually similar It actually uses metric uses evenly distance-weighted distributed, average the average of
point 7 and 8.
similarity of Ii would similarity metric equal metric divided 1. Ii divided larger by arithmetic than by arithmetic 1 illustrates average. that When When in general similar closer metric points is is evenly have distributed, higher similar
the Ii would
equal
Ii metric. Table would 1. 1 Overall Ii The equal larger result autocorrelation 1. than
Ii shows larger 1 illustrates that Index than for 1 each most that illustrates in checkpoints general that closer it general confirms points closer our have assumption points higher have similar but higher only metric. similar check The result
shows metric. point
that
7 and The for
8. result most shows checkpoints that for it most confirms checkpoints our assumption it confirms but our only assumption check point but 7 only and check 8.
Check Point 1 2 3 4 5 6 7
point 7 and 8.
Table Overall 1 Overall Correlation autocorrelation 1.87 Index for 1.99 each check point 1.33 1.35 1.33 1.12 0.49
Table Check 1 Overall Point autocorrelation 8 Index for each 9 check point 10 11 12 13 14
Check Overall Point Correlation 0.81 1 1.07 2 1.15 3 1.40 4 1.47 5 1.47 6 1.15 7
Check Overall Point Correlation 1.87 1 1.99 2 1.333 1.35 4 1.33 5 1.12 6 0.49 7
Overall Check Point Correlation 1.878 1.999 1.33 10 1.35 11 1.33 12 1.12 13 0.49 14
Check Overall One more Point Correlation thing that worth 0.818 our attention: 1.07 9 checkpoint 1.15 10 9 1.40 and 11 14 are 1.47 actually 12 the 1.47 13 same places 1.15 14 (see
Overall figure 1 Correlation on the map). However, 0.81 the 1.07 similar metric 1.15 is only 1.40 24, meaning 1.47 that only 1.47 2/3 respondents 1.15
feel it similarly when they come back to the exact same place several minutes later. So there
One more thing that worth our attention: checkpoint 9 and 14 are actually the same places (see
must be something affecting people’s feelings except the environment of the place itself.
One figure more 1 on thing the map). that worth However, our attention: the similar checkpoint metric is only 9 and 24, 14 meaning are actually that only the same 2/3 respondents places (see
figure feel it 1 similarly on the map). when However, they come the back similar to the metric exact is only same 24, place meaning several that minutes only 2/3 later. respondents So there
One
feel
more
must Creative it be similarly
thing that
Data something Mining when
worth
affecting | Spring they
our
come
attention:
2017 people’s | back Final feelings Projects to
checkpoint
the exact except same
9 and
the environment place
14 are
several
actually
of minutes
the same
the place later.
places
itself. So there
(see figure
1 on the map). However, the similar metric is only 24, meaning that only 2/3 respondents feel it similarly
must be something affecting people’s feelings except the environment of the place itself.
when they come back to the exact same place several minutes later. So there must be something
affecting Creative people’s Data Mining feelings | Spring except 2017 | the Final environment Projects of the place itself.
Creative Data Mining | Spring 2017 | Final Projects
12
New Methods in Creative Data Mining | Final project documentation