RWBP-package: Random Walk on Bipartite Graph
In RWBP: Detects spatial outliers using a Random Walk on Bipartite Graph
Description
Details
Author(s)
References
Examples
Description
Detects spatial outliers using Random Walk on Bipartite Graph technique
Details
Package: RWBP
Type: Package
Version: 1.0
Date: 2014-06-23
License: GPL (>=2)
See the example below in order to use the package.
important methods:
predict.RWBP, RWBP.formula, RWBP.default, print.RWBP, plot.RWBP
Author(s)
Sigal Shaked & Ben Nasi
Maintainer: Sigal Shaked <shaksi@post.bgu.ac.il>
References
Liu X., Lu C.T., Chen F.: Spatial outlier detection: Random walk based approaches. In: Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), San Jose, CA (2010).
Examples
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20
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42
#an example dataset:
trainSet <- cbind(
c(7.092073,7.092631,7.09263,7.093052,7.092876,7.092689,7.092515,7.092321,
7.092138,7.11455,7.11441,7.11408,7.11376,7.11338,7.11305,7.11277,7.1124,
7.11202,7.11161,7.11115,7.11068,7.11014,7.10963,7.1095,7.1089,7.10818,
7.10747,7.10674,7.116691,7.116142,7.115559,7.115007,7.114423,7.113838,
7.113272,7.112684,7.112067,7.111458,7.110869,7.110274,7.109696,7.109131,
7.109231,7.108546,7.10797,5.599215,5.597609,5.596588,5.595359,5.594478,5.593652),
c(50.77849,50.77859,50.7786,50.77878,50.77914,50.77952,50.77992,50.78035,
50.78081,53.8,53.7,53.6,53.5,54.2,55.3,55.2,56.6,57.6,57.7,58.8,59.4,59.7,
59,59.03,59.3,60.7,60.8,61.4,50.73922,50.73914,50.73905,50.73899,50.73889,
50.73881,50.73873,50.73865,50.73856,50.73847,50.73838,50.73831,50.73822,
50.73814,50.73937,50.73805,50.73798,43.2034,43.20338,43.20352,43.2037,43.20391,43.20409),
c(106.5,107.6,25,108.5,109.1,109.7,111.6,113.3,113.3,62.3,333.7,331.5,327.2,
325.5,324.8,323.5,322.3,320.3,319,317.8,316,315.1,315.3,12,312.4,311.3,310.8,
309.4,99.2,99.2,101.1,99.5,101.3,105.3,104.3,104.4,106.3,108.8,110.3,111.7,113.3,
112.1,5000,111.6,109.8,125.6,130,132.3,133.4,138,143.4),
c(0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,1,0,0,0,0,0,0,0,0)
)
colnames(trainSet)<- c("lng","lat","alt","isOutlier")
#first to columns of the input data are assumed to be spatial coordinates,
#and the rest are non-spatial attributes according to which outliers will be extracted
myRW <- RWBP(as.data.frame(trainSet[,1:3]), clusters.iterations=6)
#predict classification:
testPrediction<-predict(myRW,3 )
#calculate accuracy:
sum(testPrediction$class==trainSet[,"isOutlier"])/nrow(trainSet)
#confusion table
table(testPrediction$class, trainSet[,"isOutlier"])
#other options:
myRW1 <- RWBP(isOutlier~lng+lat+alt, data=as.data.frame(trainSet))
#print model summary
print(myRW1)
#plot model graph
plot(myRW1)
#predict probabilities of each record to be an outlier:
predict(myRW1 , top_k=4,type="prob")
Example output
Loading required package: RANN
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: lsa
Loading required package: SnowballC
[1] 0.9215686
0 1
0 46 2
1 2 1
A Random Walk on Bipartite Graph spatial outlier detection model was built:
----------------------------------------------------------------------------
neighberhood size = 10
initial clusters amount = 8
each process increases clusters amount by 2 more clusters
clusters iterations amount = 6
alfa = 0.5
dumping factor = 0.9
valid rows = 51 out of 51 input rows (records with empty values were removed)
a bipartite graph was built:
IGRAPH b066eb3 UNWB 129 306 --
+ attr: name (v/c), type (v/l), RW.Y (e/n), avgDist (e/n), weight (e/n)
+ edges from b066eb3 (vertex names):
[1] 1 ---4 2 ---4 3 ---8 4 ---4 5 ---3 6 ---3 7 ---3
[8] 8 ---3 9 ---3 10---1 11---7 12---7 13---7 14---7
[15] 15---7 16---7 17---7 18---7 19---7 20---7 21---7
[22] 22---7 23---7 24---8 25---7 26---7 27---7 28---7
[29] 29---5 30---5 31---5 32---5 33---5 34---4 35---4
[36] 36---4 37---4 38---4 39---3 40---3 41---3 42---3
[43] 43---6 44---3 45---3 46---2 47---2 48---2 49---2
[50] 50---2 51---2 1 ---1003 2 ---1006 3 ---1001 4 ---1006 5 ---1006
+ ... omitted several edges
outlier scores:
row_num outlierScore
[1,] 43 0.5748557
[2,] 10 0.6098816
[3,] 28 0.6627728
[4,] 39 0.6764593
[5,] 2 0.7598649
[6,] 3 0.8966423
[7,] 45 0.9125781
[8,] 38 0.9138153
[9,] 37 0.9138919
[10,] 42 0.9167803
[11,] 44 0.9169039
[12,] 40 0.9169214
[13,] 41 0.9171035
[14,] 1 0.9210805
[15,] 6 0.9214066
[16,] 5 0.9241858
[17,] 4 0.9244069
[18,] 7 0.9247118
[19,] 8 0.9249372
[20,] 9 0.9249372
[21,] 12 0.9440315
[22,] 11 0.9442541
[23,] 24 0.9463678
[24,] 16 0.9470852
[25,] 15 0.9473674
[26,] 13 0.9476015
[27,] 14 0.9479852
[28,] 26 0.9531505
[29,] 27 0.9552595
[30,] 25 0.9566898
[31,] 22 0.9568375
[32,] 21 0.9568599
[33,] 31 0.9630348
[34,] 33 0.9630902
[35,] 36 0.9638831
[36,] 34 0.9639474
[37,] 35 0.9641352
[38,] 32 0.9641847
[39,] 29 0.9642799
[40,] 30 0.9642799
[41,] 47 0.9901509
[42,] 51 0.9912388
[43,] 50 0.9913511
[44,] 49 0.9929033
[45,] 19 0.9937413
[46,] 17 0.9950227
[47,] 20 0.9953359
[48,] 23 0.9959121
[49,] 48 0.9961007
[50,] 46 0.9966364
[51,] 18 0.9969489
lng lat alt prob
1 7.092073 50.77849 106.5 0.1797432472
2 7.092631 50.77859 107.6 0.5616863794
3 7.092630 50.77860 25.0 0.2376407886
4 7.093052 50.77878 108.5 0.1718625080
5 7.092876 50.77914 109.1 0.1723863642
6 7.092689 50.77952 109.7 0.1789706541
7 7.092515 50.77992 111.6 0.1711401056
8 7.092321 50.78035 113.3 0.1706061464
9 7.092138 50.78081 113.3 0.1706061464
10 7.114550 53.80000 62.3 0.9170185843
11 7.114410 53.70000 333.7 0.1248415747
12 7.114080 53.60000 331.5 0.1253689921
13 7.113760 53.50000 327.2 0.1169110349
14 7.113380 54.20000 325.5 0.1160019481
15 7.113050 55.30000 324.8 0.1174656829
16 7.112770 55.20000 323.5 0.1181343974
17 7.112400 56.60000 322.3 0.0045634316
18 7.112020 57.60000 320.3 0.0000000000
19 7.111610 57.70000 319.0 0.0075991271
20 7.111150 58.80000 317.8 0.0038213684
21 7.110680 59.40000 316.0 0.0949766667
22 7.110140 59.70000 315.1 0.0950297545
23 7.109630 59.00000 315.3 0.0024563868
24 7.109500 59.03000 12.0 0.1198338045
25 7.108900 59.30000 312.4 0.0953795670
26 7.108180 60.70000 311.3 0.1037646878
27 7.107470 60.80000 310.8 0.0987681092
28 7.106740 61.40000 309.4 0.7917117025
29 7.116691 50.73922 99.2 0.0773976472
30 7.116142 50.73914 99.2 0.0773976472
31 7.115559 50.73905 101.1 0.0803474077
32 7.115007 50.73899 99.5 0.0776231584
33 7.114423 50.73889 101.3 0.0802161353
34 7.113838 50.73881 105.3 0.0781853664
35 7.113272 50.73873 104.3 0.0777404339
36 7.112684 50.73865 104.4 0.0783375265
37 7.112067 50.73856 106.3 0.1967740167
38 7.111458 50.73847 108.8 0.1969555585
39 7.110869 50.73838 110.3 0.7592862845
40 7.110274 50.73831 111.7 0.1895966810
41 7.109696 50.73822 113.3 0.1891652295
42 7.109131 50.73814 112.1 0.1899311112
43 7.109231 50.73937 5000.0 1.0000000000
44 7.108546 50.73805 111.6 0.1896381860
45 7.107970 50.73798 109.8 0.1998865257
46 5.599215 43.20340 125.6 0.0007403103
47 5.597609 43.20338 130.0 0.0161054345
48 5.596588 43.20352 132.3 0.0020094941
49 5.595359 43.20370 133.4 0.0095846352
50 5.594478 43.20391 138.0 0.0132618493
51 5.593652 43.20409 143.4 0.0135280699
RWBP documentation built on May 2, 2019, 8:39 a.m.
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Description Details Author(s) References Examples
Description
Detects spatial outliers using Random Walk on Bipartite Graph technique
Details
| Package: | RWBP |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2014-06-23 |
| License: | GPL (>=2) |
See the example below in order to use the package.
important methods:
predict.RWBP, RWBP.formula, RWBP.default, print.RWBP, plot.RWBP
Author(s)
Sigal Shaked & Ben Nasi
Maintainer: Sigal Shaked <shaksi@post.bgu.ac.il>
References
Liu X., Lu C.T., Chen F.: Spatial outlier detection: Random walk based approaches. In: Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), San Jose, CA (2010).
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | #an example dataset:
trainSet <- cbind(
c(7.092073,7.092631,7.09263,7.093052,7.092876,7.092689,7.092515,7.092321,
7.092138,7.11455,7.11441,7.11408,7.11376,7.11338,7.11305,7.11277,7.1124,
7.11202,7.11161,7.11115,7.11068,7.11014,7.10963,7.1095,7.1089,7.10818,
7.10747,7.10674,7.116691,7.116142,7.115559,7.115007,7.114423,7.113838,
7.113272,7.112684,7.112067,7.111458,7.110869,7.110274,7.109696,7.109131,
7.109231,7.108546,7.10797,5.599215,5.597609,5.596588,5.595359,5.594478,5.593652),
c(50.77849,50.77859,50.7786,50.77878,50.77914,50.77952,50.77992,50.78035,
50.78081,53.8,53.7,53.6,53.5,54.2,55.3,55.2,56.6,57.6,57.7,58.8,59.4,59.7,
59,59.03,59.3,60.7,60.8,61.4,50.73922,50.73914,50.73905,50.73899,50.73889,
50.73881,50.73873,50.73865,50.73856,50.73847,50.73838,50.73831,50.73822,
50.73814,50.73937,50.73805,50.73798,43.2034,43.20338,43.20352,43.2037,43.20391,43.20409),
c(106.5,107.6,25,108.5,109.1,109.7,111.6,113.3,113.3,62.3,333.7,331.5,327.2,
325.5,324.8,323.5,322.3,320.3,319,317.8,316,315.1,315.3,12,312.4,311.3,310.8,
309.4,99.2,99.2,101.1,99.5,101.3,105.3,104.3,104.4,106.3,108.8,110.3,111.7,113.3,
112.1,5000,111.6,109.8,125.6,130,132.3,133.4,138,143.4),
c(0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,1,0,0,0,0,0,0,0,0)
)
colnames(trainSet)<- c("lng","lat","alt","isOutlier")
#first to columns of the input data are assumed to be spatial coordinates,
#and the rest are non-spatial attributes according to which outliers will be extracted
myRW <- RWBP(as.data.frame(trainSet[,1:3]), clusters.iterations=6)
#predict classification:
testPrediction<-predict(myRW,3 )
#calculate accuracy:
sum(testPrediction$class==trainSet[,"isOutlier"])/nrow(trainSet)
#confusion table
table(testPrediction$class, trainSet[,"isOutlier"])
#other options:
myRW1 <- RWBP(isOutlier~lng+lat+alt, data=as.data.frame(trainSet))
#print model summary
print(myRW1)
#plot model graph
plot(myRW1)
#predict probabilities of each record to be an outlier:
predict(myRW1 , top_k=4,type="prob")
|
Example output
Loading required package: RANN
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: lsa
Loading required package: SnowballC
[1] 0.9215686
0 1
0 46 2
1 2 1
A Random Walk on Bipartite Graph spatial outlier detection model was built:
----------------------------------------------------------------------------
neighberhood size = 10
initial clusters amount = 8
each process increases clusters amount by 2 more clusters
clusters iterations amount = 6
alfa = 0.5
dumping factor = 0.9
valid rows = 51 out of 51 input rows (records with empty values were removed)
a bipartite graph was built:
IGRAPH b066eb3 UNWB 129 306 --
+ attr: name (v/c), type (v/l), RW.Y (e/n), avgDist (e/n), weight (e/n)
+ edges from b066eb3 (vertex names):
[1] 1 ---4 2 ---4 3 ---8 4 ---4 5 ---3 6 ---3 7 ---3
[8] 8 ---3 9 ---3 10---1 11---7 12---7 13---7 14---7
[15] 15---7 16---7 17---7 18---7 19---7 20---7 21---7
[22] 22---7 23---7 24---8 25---7 26---7 27---7 28---7
[29] 29---5 30---5 31---5 32---5 33---5 34---4 35---4
[36] 36---4 37---4 38---4 39---3 40---3 41---3 42---3
[43] 43---6 44---3 45---3 46---2 47---2 48---2 49---2
[50] 50---2 51---2 1 ---1003 2 ---1006 3 ---1001 4 ---1006 5 ---1006
+ ... omitted several edges
outlier scores:
row_num outlierScore
[1,] 43 0.5748557
[2,] 10 0.6098816
[3,] 28 0.6627728
[4,] 39 0.6764593
[5,] 2 0.7598649
[6,] 3 0.8966423
[7,] 45 0.9125781
[8,] 38 0.9138153
[9,] 37 0.9138919
[10,] 42 0.9167803
[11,] 44 0.9169039
[12,] 40 0.9169214
[13,] 41 0.9171035
[14,] 1 0.9210805
[15,] 6 0.9214066
[16,] 5 0.9241858
[17,] 4 0.9244069
[18,] 7 0.9247118
[19,] 8 0.9249372
[20,] 9 0.9249372
[21,] 12 0.9440315
[22,] 11 0.9442541
[23,] 24 0.9463678
[24,] 16 0.9470852
[25,] 15 0.9473674
[26,] 13 0.9476015
[27,] 14 0.9479852
[28,] 26 0.9531505
[29,] 27 0.9552595
[30,] 25 0.9566898
[31,] 22 0.9568375
[32,] 21 0.9568599
[33,] 31 0.9630348
[34,] 33 0.9630902
[35,] 36 0.9638831
[36,] 34 0.9639474
[37,] 35 0.9641352
[38,] 32 0.9641847
[39,] 29 0.9642799
[40,] 30 0.9642799
[41,] 47 0.9901509
[42,] 51 0.9912388
[43,] 50 0.9913511
[44,] 49 0.9929033
[45,] 19 0.9937413
[46,] 17 0.9950227
[47,] 20 0.9953359
[48,] 23 0.9959121
[49,] 48 0.9961007
[50,] 46 0.9966364
[51,] 18 0.9969489
lng lat alt prob
1 7.092073 50.77849 106.5 0.1797432472
2 7.092631 50.77859 107.6 0.5616863794
3 7.092630 50.77860 25.0 0.2376407886
4 7.093052 50.77878 108.5 0.1718625080
5 7.092876 50.77914 109.1 0.1723863642
6 7.092689 50.77952 109.7 0.1789706541
7 7.092515 50.77992 111.6 0.1711401056
8 7.092321 50.78035 113.3 0.1706061464
9 7.092138 50.78081 113.3 0.1706061464
10 7.114550 53.80000 62.3 0.9170185843
11 7.114410 53.70000 333.7 0.1248415747
12 7.114080 53.60000 331.5 0.1253689921
13 7.113760 53.50000 327.2 0.1169110349
14 7.113380 54.20000 325.5 0.1160019481
15 7.113050 55.30000 324.8 0.1174656829
16 7.112770 55.20000 323.5 0.1181343974
17 7.112400 56.60000 322.3 0.0045634316
18 7.112020 57.60000 320.3 0.0000000000
19 7.111610 57.70000 319.0 0.0075991271
20 7.111150 58.80000 317.8 0.0038213684
21 7.110680 59.40000 316.0 0.0949766667
22 7.110140 59.70000 315.1 0.0950297545
23 7.109630 59.00000 315.3 0.0024563868
24 7.109500 59.03000 12.0 0.1198338045
25 7.108900 59.30000 312.4 0.0953795670
26 7.108180 60.70000 311.3 0.1037646878
27 7.107470 60.80000 310.8 0.0987681092
28 7.106740 61.40000 309.4 0.7917117025
29 7.116691 50.73922 99.2 0.0773976472
30 7.116142 50.73914 99.2 0.0773976472
31 7.115559 50.73905 101.1 0.0803474077
32 7.115007 50.73899 99.5 0.0776231584
33 7.114423 50.73889 101.3 0.0802161353
34 7.113838 50.73881 105.3 0.0781853664
35 7.113272 50.73873 104.3 0.0777404339
36 7.112684 50.73865 104.4 0.0783375265
37 7.112067 50.73856 106.3 0.1967740167
38 7.111458 50.73847 108.8 0.1969555585
39 7.110869 50.73838 110.3 0.7592862845
40 7.110274 50.73831 111.7 0.1895966810
41 7.109696 50.73822 113.3 0.1891652295
42 7.109131 50.73814 112.1 0.1899311112
43 7.109231 50.73937 5000.0 1.0000000000
44 7.108546 50.73805 111.6 0.1896381860
45 7.107970 50.73798 109.8 0.1998865257
46 5.599215 43.20340 125.6 0.0007403103
47 5.597609 43.20338 130.0 0.0161054345
48 5.596588 43.20352 132.3 0.0020094941
49 5.595359 43.20370 133.4 0.0095846352
50 5.594478 43.20391 138.0 0.0132618493
51 5.593652 43.20409 143.4 0.0135280699
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