Lotto Number Recommend with Association Rules Using R
The number of lotto Recommend Systems with R & Analytics
- 1 ~ 730회차 Lotto Numbers
- Association Rules
Load Library & Data
library(arules)
library(reshape2)
lotte <- read.csv("lotto.csv")
lotte <- lotte[,-8]
colnames(lotte) <- c("seq","N1","N2","N3","N4","N5","N6")
회차 별 뽑힌 숫자.
- 보너스 숫자 제외
print(head(lotte[order(lotte$seq, decreasing = F),]))
seq N1 N2 N3 N4 N5 N6
730 1 10 23 29 33 37 40
729 2 9 13 21 25 32 42
728 3 11 16 19 21 27 31
727 4 14 27 30 31 40 42
726 5 16 24 29 40 41 42
725 6 14 15 26 27 40 42
Data Reshaping
- \(Transacition\ Data\) 형성을 위해 \(Reshape\) (\(Melt\)를 \(seq\)를 기준으로 수행)
melt_lotte <- melt(lotte, id="seq") # seq(회차)를 기준으로 데이터 Melt
print(melt_lotte[melt_lotte$seq ==730,]) # 확인
seq variable value
1 730 N1 4
731 730 N2 10
1461 730 N3 14
2191 730 N4 15
2921 730 N5 18
3651 730 N6 22
Pick data from DF
data <- melt_lotte[,c(1,3)] # seq, value
print(head(data[order(data$seq,decreasing = T),]))
seq value
1 730 4
731 730 10
1461 730 14
2191 730 15
2921 730 18
3651 730 22
Split Data with seq number
- \(Value\)를 \(Seq\)로 \(Split\)한다.
print(head(split(data$value, data$seq)))
$`1`
[1] 10 23 29 33 37 40
$`2`
[1] 9 13 21 25 32 42
$`3`
[1] 11 16 19 21 27 31
$`4`
[1] 14 27 30 31 40 42
$`5`
[1] 16 24 29 40 41 42
$`6`
[1] 14 15 26 27 40 42
Make Transactions
trans <- as(split(data$value, data$seq), "transactions") #transactions 메소드
trans
transactions in sparse format with
730 transactions (rows) and
45 items (columns)
Inspect a transaction
- 각 회차별 나온 숫자를 Transaction으로 변형
print(inspect(trans[1:10]))
items transactionID
[1] {10,23,29,33,37,40} 1
[2] {9,13,21,25,32,42} 2
[3] {11,16,19,21,27,31} 3
[4] {14,27,30,31,40,42} 4
[5] {16,24,29,40,41,42} 5
[6] {14,15,26,27,40,42} 6
[7] {2,9,16,25,26,40} 7
[8] {8,19,25,34,37,39} 8
[9] {2,4,16,17,36,39} 9
[10] {9,25,30,33,41,44} 10
items transactionID
[1] {10,23,29,33,37,40} 1
[2] {9,13,21,25,32,42} 2
[3] {11,16,19,21,27,31} 3
[4] {14,27,30,31,40,42} 4
[5] {16,24,29,40,41,42} 5
[6] {14,15,26,27,40,42} 6
[7] {2,9,16,25,26,40} 7
[8] {8,19,25,34,37,39} 8
[9] {2,4,16,17,36,39} 9
[10] {9,25,30,33,41,44} 10
Image of Transaction
- 1 ~ 45 까지 숫자 중 각 회차별 새당 되는 숫자에 색이 칠해진다.
options(repr.plot.width=4,repr.plot.height=2)
image(trans[1:10])
Check Frequency of items
- 각 회차에 나온 개별의 숫자들의 빈발 정도를 확인 (absolute : Counts of values)
print(t(itemFrequency(trans, type="absolute")))
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
[1,] 109 95 96 105 102 91 100 104 72 99 101 94 101 105 99 89 105 100 98 116 90
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[1,] 82 90 95 99 100 111 85 91 87 98 85 101 113 92 96 108 91 99 114 86 87 101
44 45
[1,] 100 98
Check Posibility of items
- 각 회차에 나온 개별 숫자들의 나온 확률을 확인
print(t(round(itemFrequency(trans)[order(itemFrequency(trans), decreasing = TRUE)],2)))
20 40 34 27 1 37 4 14 17 8 5 11 13 33 43
[1,] 0.16 0.16 0.15 0.15 0.15 0.15 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14
7 18 26 44 10 15 25 39 19 31 45 3 36 2 24
[1,] 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13
12 35 6 29 38 21 23 16 30 42 41 28 32 22 9
[1,] 0.13 0.13 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.1
Plotting items with support
- 상위 20개 지지도를 가진 Items를 Plotting
options(repr.plot.width=4,repr.plot.height=4)
itemFrequencyPlot(trans, topN = 20, main = "support top 20 items",cex.names=0.6)
Making rules with transaction data, Lotte
- 최소 지지도를 넘는 빈발 집합을 출력.
- Transaction ID 는 필요 없으므로 제외
rules <- apriori(trans[,-2],parameter = list(support=0.005,target="frequent itemsets"))
Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
NA 0.1 1 none FALSE TRUE 5 0.005 1
maxlen target ext
10 frequent itemsets FALSE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 3
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[44 item(s), 730 transaction(s)] done [0.00s].
sorting and recoding items ... [44 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 done [0.00s].
writing ... [1241 set(s)] done [0.00s].
creating S4 object ... done [0.00s].
- 크기 1 : 44개
- 크기 2 : 941개
- 크기 3 : 256개
summary of quality measures
- 최소 지지도 : 0.0055
- 최대 지지도 : 0.1589
summary(rules)
set of 1241 itemsets
most frequent items:
40 20 27 7 35 (Other)
76 74 70 68 68 2338
element (itemset/transaction) length distribution:sizes
1 2 3
44 941 256
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 2.000 2.000 2.171 2.000 3.000
summary of quality measures:
support
Min. :0.005479
1st Qu.:0.008219
Median :0.013699
Mean :0.017469
3rd Qu.:0.017808
Max. :0.158904
includes transaction ID lists: FALSE
mining info:
data ntransactions support confidence
trans[, -2] 730 0.005 1
inspect(rules[1:10])
items support
[1] {9} 0.09863014
[2] {22} 0.11232877
[3] {28} 0.11643836
[4] {32} 0.11643836
[5] {41} 0.11780822
[6] {42} 0.11917808
[7] {30} 0.11917808
[8] {16} 0.12191781
[9] {21} 0.12328767
[10] {23} 0.12328767
Top 10 of the Support
inspect(sort(rules, by = "support")[1:10])
items support
[1] {20} 0.1589041
[2] {40} 0.1561644
[3] {34} 0.1547945
[4] {27} 0.1520548
[5] {1} 0.1493151
[6] {37} 0.1479452
[7] {17} 0.1438356
[8] {4} 0.1438356
[9] {14} 0.1438356
[10] {8} 0.1424658
2 Set of Rules
inspect(sort(rules, by = "support")[43:50])
items support
[1] {22} 0.11232877
[2] {9} 0.09863014
[3] {20,35} 0.02876712
[4] {3,20} 0.02876712
[5] {31,34} 0.02876712
[6] {8,39} 0.02876712
[7] {33,40} 0.02876712
[8] {27,40} 0.02876712
Find 3 Set of Rules
- {19,25,28} 0.006849315 is Maximum support 3 Set
inspect(sort(rules[rules@quality$support >= 0.005 & rules@quality$support <= 0.00685], by = "support")[25:40])
items support
[1] {24,43} 0.006849315
[2] {31,39} 0.006849315
[3] {19,25,28} 0.006849315
[4] {15,28,34} 0.006849315
[5] {4,28,40} 0.006849315
[6] {10,16,41} 0.006849315
[7] {34,42,45} 0.006849315
[8] {5,18,42} 0.006849315
[9] {14,27,30} 0.006849315
[10] {11,14,21} 0.006849315
[11] {23,29,44} 0.006849315
[12] {23,35,43} 0.006849315
[13] {6,7,15} 0.006849315
[14] {6,18,31} 0.006849315
[15] {11,29,44} 0.006849315
[16] {27,29,40} 0.006849315
df <- as.data.frame(inspect(sort(rules, by = "support")))
items support
[1] {20} 0.158904110
[2] {40} 0.156164384
[3] {34} 0.154794521
[4] {27} 0.152054795
[5] {1} 0.149315068
[6] {37} 0.147945205
[7] {17} 0.143835616
[8] {4} 0.143835616
[9] {14} 0.143835616
[10] {8} 0.142465753
[11] {5} 0.139726027
[12] {33} 0.138356164
[13] {11} 0.138356164
[14] {43} 0.138356164
[15] {13} 0.138356164
[16] {44} 0.136986301
[17] {7} 0.136986301
[18] {18} 0.136986301
[19] {26} 0.136986301
[20] {25} 0.135616438
[21] {15} 0.135616438
[22] {39} 0.135616438
[23] {10} 0.135616438
[24] {19} 0.134246575
[25] {45} 0.134246575
[26] {31} 0.134246575
[27] {3} 0.131506849
[28] {36} 0.131506849
[29] {24} 0.130136986
[30] {12} 0.128767123
[31] {35} 0.126027397
[32] {6} 0.124657534
[33] {38} 0.124657534
[34] {29} 0.124657534
[35] {21} 0.123287671
[36] {23} 0.123287671
[37] {16} 0.121917808
[38] {42} 0.119178082
[39] {30} 0.119178082
[40] {41} 0.117808219
[41] {28} 0.116438356
[42] {32} 0.116438356
[43] {22} 0.112328767
[44] {9} 0.098630137
[45] {20,35} 0.028767123
[46] {3,20} 0.028767123
[47] {31,34} 0.028767123
[48] {8,39} 0.028767123
[49] {33,40} 0.028767123
[50] {27,40} 0.028767123
[51] {11,21} 0.027397260
[52] {7,20} 0.027397260
[53] {11,26} 0.027397260
[54] {22,37} 0.026027397
[55] {17,31} 0.026027397
[56] {31,40} 0.026027397
[57] {7,18} 0.026027397
[58] {7,40} 0.026027397
[59] {5,20} 0.026027397
[60] {8,27} 0.026027397
[61] {4,20} 0.026027397
[62] {1,34} 0.026027397
[63] {1,28} 0.024657534
[64] {1,42} 0.024657534
[65] {27,35} 0.024657534
[66] {12,24} 0.024657534
[67] {12,15} 0.024657534
[68] {24,27} 0.024657534
[69] {14,15} 0.024657534
[70] {10,31} 0.024657534
[71] {14,39} 0.024657534
[72] {20,33} 0.024657534
[73] {5,34} 0.024657534
[74] {17,20} 0.024657534
[75] {4,40} 0.024657534
[76] {37,40} 0.024657534
[77] {6,28} 0.023287671
[78] {16,41} 0.023287671
[79] {14,21} 0.023287671
[80] {18,23} 0.023287671
[81] {29,44} 0.023287671
[82] {35,40} 0.023287671
[83] {3,24} 0.023287671
[84] {19,25} 0.023287671
[85] {19,43} 0.023287671
[86] {17,45} 0.023287671
[87] {15,34} 0.023287671
[88] {18,31} 0.023287671
[89] {11,39} 0.023287671
[90] {34,44} 0.023287671
[91] {4,10} 0.023287671
[92] {26,43} 0.023287671
[93] {26,27} 0.023287671
[94] {13,33} 0.023287671
[95] {4,33} 0.023287671
[96] {11,37} 0.023287671
[97] {37,43} 0.023287671
[98] {5,14} 0.023287671
[99] {5,27} 0.023287671
[100] {1,8} 0.023287671
[101] {1,17} 0.023287671
[102] {4,34} 0.023287671
[103] {1,20} 0.023287671
[104] {28,40} 0.021917808
[105] {3,32} 0.021917808
[106] {14,32} 0.021917808
[107] {33,41} 0.021917808
[108] {30,45} 0.021917808
[109] {14,30} 0.021917808
[110] {20,23} 0.021917808
[111] {29,33} 0.021917808
[112] {1,3} 0.021917808
[113] {3,27} 0.021917808
[114] {25,36} 0.021917808
[115] {36,39} 0.021917808
[116] {17,36} 0.021917808
[117] {10,19} 0.021917808
[118] {19,34} 0.021917808
[119] {37,45} 0.021917808
[120] {7,15} 0.021917808
[121] {10,44} 0.021917808
[122] {1,10} 0.021917808
[123] {10,40} 0.021917808
[124] {18,26} 0.021917808
[125] {8,18} 0.021917808
[126] {4,26} 0.021917808
[127] {8,13} 0.021917808
[128] {13,37} 0.021917808
[129] {1,40} 0.021917808
[130] {11,28} 0.020547945
[131] {12,32} 0.020547945
[132] {18,32} 0.020547945
[133] {41,45} 0.020547945
[134] {40,41} 0.020547945
[135] {19,42} 0.020547945
[136] {30,38} 0.020547945
[137] {16,29} 0.020547945
[138] {23,44} 0.020547945
[139] {6,38} 0.020547945
[140] {6,13} 0.020547945
[141] {5,6} 0.020547945
[142] {6,40} 0.020547945
[143] {8,38} 0.020547945
[144] {37,38} 0.020547945
[145] {38,40} 0.020547945
[146] {29,43} 0.020547945
[147] {1,29} 0.020547945
[148] {35,43} 0.020547945
[149] {12,40} 0.020547945
[150] {24,44} 0.020547945
[151] {20,24} 0.020547945
[152] {3,11} 0.020547945
[153] {3,14} 0.020547945
[154] {3,37} 0.020547945
[155] {4,19} 0.020547945
[156] {19,20} 0.020547945
[157] {18,45} 0.020547945
[158] {8,45} 0.020547945
[159] {34,45} 0.020547945
[160] {4,25} 0.020547945
[161] {15,26} 0.020547945
[162] {11,15} 0.020547945
[163] {31,44} 0.020547945
[164] {14,31} 0.020547945
[165] {27,31} 0.020547945
[166] {17,39} 0.020547945
[167] {11,44} 0.020547945
[168] {20,44} 0.020547945
[169] {7,33} 0.020547945
[170] {1,18} 0.020547945
[171] {18,34} 0.020547945
[172] {14,26} 0.020547945
[173] {26,40} 0.020547945
[174] {20,26} 0.020547945
[175] {33,37} 0.020547945
[176] {11,13} 0.020547945
[177] {5,11} 0.020547945
[178] {27,43} 0.020547945
[179] {13,20} 0.020547945
[180] {1,5} 0.020547945
[181] {4,27} 0.020547945
[182] {1,37} 0.020547945
[183] {19,28} 0.019178082
[184] {13,28} 0.019178082
[185] {39,41} 0.019178082
[186] {41,44} 0.019178082
[187] {15,42} 0.019178082
[188] {5,42} 0.019178082
[189] {27,42} 0.019178082
[190] {24,30} 0.019178082
[191] {30,43} 0.019178082
[192] {8,30} 0.019178082
[193] {17,30} 0.019178082
[194] {30,34} 0.019178082
[195] {20,30} 0.019178082
[196] {10,16} 0.019178082
[197] {4,16} 0.019178082
[198] {16,40} 0.019178082
[199] {18,21} 0.019178082
[200] {21,26} 0.019178082
[201] {8,21} 0.019178082
[202] {21,34} 0.019178082
[203] {23,35} 0.019178082
[204] {4,23} 0.019178082
[205] {6,31} 0.019178082
[206] {26,29} 0.019178082
[207] {27,29} 0.019178082
[208] {33,35} 0.019178082
[209] {17,35} 0.019178082
[210] {35,37} 0.019178082
[211] {12,27} 0.019178082
[212] {12,34} 0.019178082
[213] {12,20} 0.019178082
[214] {24,33} 0.019178082
[215] {3,13} 0.019178082
[216] {36,44} 0.019178082
[217] {33,36} 0.019178082
[218] {8,36} 0.019178082
[219] {14,36} 0.019178082
[220] {20,36} 0.019178082
[221] {19,45} 0.019178082
[222] {14,45} 0.019178082
[223] {15,25} 0.019178082
[224] {25,33} 0.019178082
[225] {5,25} 0.019178082
[226] {25,37} 0.019178082
[227] {25,34} 0.019178082
[228] {15,43} 0.019178082
[229] {15,20} 0.019178082
[230] {8,31} 0.019178082
[231] {7,39} 0.019178082
[232] {18,39} 0.019178082
[233] {27,39} 0.019178082
[234] {8,44} 0.019178082
[235] {17,44} 0.019178082
[236] {10,37} 0.019178082
[237] {7,43} 0.019178082
[238] {7,37} 0.019178082
[239] {8,33} 0.019178082
[240] {11,14} 0.019178082
[241] {8,43} 0.019178082
[242] {20,43} 0.019178082
[243] {5,13} 0.019178082
[244] {13,14} 0.019178082
[245] {4,5} 0.019178082
[246] {8,17} 0.019178082
[247] {8,34} 0.019178082
[248] {17,34} 0.019178082
[249] {4,37} 0.019178082
[250] {14,40} 0.019178082
[251] {1,27} 0.019178082
[252] {20,40} 0.019178082
[253] {5,9} 0.017808219
[254] {22,36} 0.017808219
[255] {22,25} 0.017808219
[256] {10,22} 0.017808219
[257] {14,22} 0.017808219
[258] {22,34} 0.017808219
[259] {28,34} 0.017808219
[260] {32,45} 0.017808219
[261] {10,32} 0.017808219
[262] {32,33} 0.017808219
[263] {11,32} 0.017808219
[264] {13,32} 0.017808219
[265] {17,32} 0.017808219
[266] {32,40} 0.017808219
[267] {7,41} 0.017808219
[268] {4,41} 0.017808219
[269] {3,42} 0.017808219
[270] {42,45} 0.017808219
[271] {7,42} 0.017808219
[272] {34,42} 0.017808219
[273] {27,30} 0.017808219
[274] {16,36} 0.017808219
[275] {16,25} 0.017808219
[276] {7,16} 0.017808219
[277] {16,26} 0.017808219
[278] {16,17} 0.017808219
[279] {1,16} 0.017808219
[280] {21,36} 0.017808219
[281] {15,21} 0.017808219
[282] {21,31} 0.017808219
[283] {23,29} 0.017808219
[284] {15,23} 0.017808219
[285] {6,39} 0.017808219
[286] {6,10} 0.017808219
[287] {6,37} 0.017808219
[288] {12,38} 0.017808219
[289] {7,38} 0.017808219
[290] {25,29} 0.017808219
[291] {7,29} 0.017808219
[292] {26,35} 0.017808219
[293] {1,35} 0.017808219
[294] {1,12} 0.017808219
[295] {10,24} 0.017808219
[296] {7,24} 0.017808219
[297] {24,40} 0.017808219
[298] {3,7} 0.017808219
[299] {3,4} 0.017808219
[300] {36,45} 0.017808219
[301] {11,36} 0.017808219
[302] {34,36} 0.017808219
[303] {19,26} 0.017808219
[304] {5,19} 0.017808219
[305] {17,19} 0.017808219
[306] {39,45} 0.017808219
[307] {25,43} 0.017808219
[308] {13,25} 0.017808219
[309] {13,15} 0.017808219
[310] {11,31} 0.017808219
[311] {31,37} 0.017808219
[312] {39,44} 0.017808219
[313] {5,39} 0.017808219
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[819] {10,42} 0.010958904
[820] {30,35} 0.010958904
[821] {12,30} 0.010958904
[822] {15,30} 0.010958904
[823] {30,44} 0.010958904
[824] {10,30} 0.010958904
[825] {13,30} 0.010958904
[826] {16,35} 0.010958904
[827] {3,16} 0.010958904
[828] {16,33} 0.010958904
[829] {16,27} 0.010958904
[830] {7,21} 0.010958904
[831] {21,40} 0.010958904
[832] {20,21} 0.010958904
[833] {12,23} 0.010958904
[834] {3,23} 0.010958904
[835] {23,33} 0.010958904
[836] {23,37} 0.010958904
[837] {6,35} 0.010958904
[838] {4,38} 0.010958904
[839] {29,31} 0.010958904
[840] {12,35} 0.010958904
[841] {11,35} 0.010958904
[842] {4,35} 0.010958904
[843] {12,17} 0.010958904
[844] {19,24} 0.010958904
[845] {24,31} 0.010958904
[846] {24,39} 0.010958904
[847] {3,18} 0.010958904
[848] {3,33} 0.010958904
[849] {19,36} 0.010958904
[850] {13,36} 0.010958904
[851] {19,39} 0.010958904
[852] {7,45} 0.010958904
[853] {26,45} 0.010958904
[854] {18,25} 0.010958904
[855] {1,31} 0.010958904
[856] {13,44} 0.010958904
[857] {10,43} 0.010958904
[858] {10,13} 0.010958904
[859] {5,10} 0.010958904
[860] {7,13} 0.010958904
[861] {7,14} 0.010958904
[862] {33,43} 0.010958904
[863] {11,20} 0.010958904
[864] {5,40} 0.010958904
[865] {9,23} 0.009589041
[866] {9,36} 0.009589041
[867] {18,22} 0.009589041
[868] {22,26} 0.009589041
[869] {8,22} 0.009589041
[870] {1,22} 0.009589041
[871] {28,41} 0.009589041
[872] {24,28} 0.009589041
[873] {18,28} 0.009589041
[874] {28,33} 0.009589041
[875] {23,32} 0.009589041
[876] {32,35} 0.009589041
[877] {32,36} 0.009589041
[878] {15,32} 0.009589041
[879] {7,32} 0.009589041
[880] {21,41} 0.009589041
[881] {38,41} 0.009589041
[882] {35,41} 0.009589041
[883] {5,41} 0.009589041
[884] {38,42} 0.009589041
[885] {35,42} 0.009589041
[886] {31,42} 0.009589041
[887] {42,44} 0.009589041
[888] {11,30} 0.009589041
[889] {1,30} 0.009589041
[890] {16,21} 0.009589041
[891] {15,16} 0.009589041
[892] {16,39} 0.009589041
[893] {16,18} 0.009589041
[894] {3,21} 0.009589041
[895] {23,24} 0.009589041
[896] {23,31} 0.009589041
[897] {17,23} 0.009589041
[898] {3,6} 0.009589041
[899] {6,27} 0.009589041
[900] {35,38} 0.009589041
[901] {15,29} 0.009589041
[902] {14,29} 0.009589041
[903] {7,35} 0.009589041
[904] {12,31} 0.009589041
[905] {14,24} 0.009589041
[906] {3,19} 0.009589041
[907] {3,39} 0.009589041
[908] {3,26} 0.009589041
[909] {4,36} 0.009589041
[910] {11,19} 0.009589041
[911] {44,45} 0.009589041
[912] {10,45} 0.009589041
[913] {11,25} 0.009589041
[914] {15,31} 0.009589041
[915] {1,39} 0.009589041
[916] {4,44} 0.009589041
[917] {7,11} 0.009589041
[918] {4,7} 0.009589041
[919] {5,33} 0.009589041
[920] {11,34} 0.009589041
[921] {8,40} 0.009589041
[922] {9,22} 0.008219178
[923] {9,42} 0.008219178
[924] {9,45} 0.008219178
[925] {9,13} 0.008219178
[926] {9,20} 0.008219178
[927] {22,30} 0.008219178
[928] {16,22} 0.008219178
[929] {22,29} 0.008219178
[930] {12,22} 0.008219178
[931] {22,45} 0.008219178
[932] {28,29} 0.008219178
[933] {32,38} 0.008219178
[934] {32,39} 0.008219178
[935] {23,41} 0.008219178
[936] {14,41} 0.008219178
[937] {39,42} 0.008219178
[938] {4,30} 0.008219178
[939] {13,16} 0.008219178
[940] {8,16} 0.008219178
[941] {14,16} 0.008219178
[942] {21,38} 0.008219178
[943] {21,35} 0.008219178
[944] {10,21} 0.008219178
[945] {6,45} 0.008219178
[946] {15,38} 0.008219178
[947] {5,38} 0.008219178
[948] {29,45} 0.008219178
[949] {18,35} 0.008219178
[950] {3,25} 0.008219178
[951] {19,33} 0.008219178
[952] {19,37} 0.008219178
[953] {15,33} 0.008219178
[954] {11,33} 0.008219178
[955] {14,30,38} 0.008219178
[956] {7,18,23} 0.008219178
[957] {20,26,35} 0.008219178
[958] {12,24,27} 0.008219178
[959] {3,20,24} 0.008219178
[960] {11,26,44} 0.008219178
[961] {9,30} 0.006849315
[962] {3,9} 0.006849315
[963] {9,19} 0.006849315
[964] {9,44} 0.006849315
[965] {9,18} 0.006849315
[966] {9,14} 0.006849315
[967] {22,32} 0.006849315
[968] {16,28} 0.006849315
[969] {28,31} 0.006849315
[970] {14,28} 0.006849315
[971] {30,32} 0.006849315
[972] {16,32} 0.006849315
[973] {32,43} 0.006849315
[974] {18,41} 0.006849315
[975] {21,42} 0.006849315
[976] {6,42} 0.006849315
[977] {11,42} 0.006849315
[978] {29,30} 0.006849315
[979] {25,30} 0.006849315
[980] {6,23} 0.006849315
[981] {6,29} 0.006849315
[982] {6,36} 0.006849315
[983] {6,33} 0.006849315
[984] {19,29} 0.006849315
[985] {24,43} 0.006849315
[986] {31,39} 0.006849315
[987] {19,25,28} 0.006849315
[988] {15,28,34} 0.006849315
[989] {4,28,40} 0.006849315
[990] {10,16,41} 0.006849315
[991] {34,42,45} 0.006849315
[992] {5,18,42} 0.006849315
[993] {14,27,30} 0.006849315
[994] {11,14,21} 0.006849315
[995] {23,29,44} 0.006849315
[996] {23,35,43} 0.006849315
[997] {6,7,15} 0.006849315
[998] {6,18,31} 0.006849315
[999] {11,29,44} 0.006849315
[1000] {27,29,40} 0.006849315
[1001] {35,43,45} 0.006849315
[1002] {14,35,39} 0.006849315
[1003] {20,35,40} 0.006849315
[1004] {12,15,24} 0.006849315
[1005] {4,12,24} 0.006849315
[1006] {12,33,40} 0.006849315
[1007] {3,20,44} 0.006849315
[1008] {3,8,27} 0.006849315
[1009] {17,26,36} 0.006849315
[1010] {4,10,19} 0.006849315
[1011] {19,26,27} 0.006849315
[1012] {4,19,20} 0.006849315
[1013] {8,18,45} 0.006849315
[1014] {17,34,45} 0.006849315
[1015] {7,20,25} 0.006849315
[1016] {7,15,43} 0.006849315
[1017] {31,34,44} 0.006849315
[1018] {18,31,34} 0.006849315
[1019] {17,20,31} 0.006849315
[1020] {10,11,39} 0.006849315
[1021] {8,34,39} 0.006849315
[1022] {7,33,40} 0.006849315
[1023] {13,33,37} 0.006849315
[1024] {4,33,40} 0.006849315
[1025] {33,37,40} 0.006849315
[1026] {5,14,20} 0.006849315
[1027] {1,5,34} 0.006849315
[1028] {8,9} 0.005479452
[1029] {3,28} 0.005479452
[1030] {37,44} 0.005479452
[1031] {8,26} 0.005479452
[1032] {11,40} 0.005479452
[1033] {1,9,21} 0.005479452
[1034] {1,9,17} 0.005479452
[1035] {5,22,42} 0.005479452
[1036] {16,22,37} 0.005479452
[1037] {21,22,37} 0.005479452
[1038] {22,35,40} 0.005479452
[1039] {7,22,40} 0.005479452
[1040] {7,20,22} 0.005479452
[1041] {22,27,40} 0.005479452
[1042] {1,28,41} 0.005479452
[1043] {6,11,28} 0.005479452
[1044] {6,17,28} 0.005479452
[1045] {1,6,28} 0.005479452
[1046] {6,28,40} 0.005479452
[1047] {19,28,38} 0.005479452
[1048] {11,26,28} 0.005479452
[1049] {8,13,28} 0.005479452
[1050] {1,17,28} 0.005479452
[1051] {1,28,34} 0.005479452
[1052] {1,28,40} 0.005479452
[1053] {27,28,40} 0.005479452
[1054] {32,40,41} 0.005479452
[1055] {13,32,42} 0.005479452
[1056] {6,31,32} 0.005479452
[1057] {12,13,32} 0.005479452
[1058] {3,24,32} 0.005479452
[1059] {3,32,45} 0.005479452
[1060] {1,3,32} 0.005479452
[1061] {19,20,32} 0.005479452
[1062] {18,31,32} 0.005479452
[1063] {4,10,32} 0.005479452
[1064] {18,32,43} 0.005479452
[1065] {32,33,40} 0.005479452
[1066] {11,14,32} 0.005479452
[1067] {14,32,37} 0.005479452
[1068] {16,29,41} 0.005479452
[1069] {16,40,41} 0.005479452
[1070] {3,40,41} 0.005479452
[1071] {36,41,44} 0.005479452
[1072] {20,36,41} 0.005479452
[1073] {39,41,45} 0.005479452
[1074] {7,39,41} 0.005479452
[1075] {33,41,44} 0.005479452
[1076] {33,40,41} 0.005479452
[1077] {13,40,41} 0.005479452
[1078] {17,20,41} 0.005479452
[1079] {16,42,45} 0.005479452
[1080] {24,27,42} 0.005479452
[1081] {3,8,42} 0.005479452
[1082] {3,14,42} 0.005479452
[1083] {19,27,42} 0.005479452
[1084] {7,42,45} 0.005479452
[1085] {15,26,42} 0.005479452
[1086] {1,7,42} 0.005479452
[1087] {7,37,42} 0.005479452
[1088] {26,27,42} 0.005479452
[1089] {30,38,45} 0.005479452
[1090] {17,30,38} 0.005479452
[1091] {17,30,31} 0.005479452
[1092] {14,30,31} 0.005479452
[1093] {30,39,43} 0.005479452
[1094] {8,30,43} 0.005479452
[1095] {17,20,30} 0.005479452
[1096] {16,23,25} 0.005479452
[1097] {16,29,44} 0.005479452
[1098] {4,16,29} 0.005479452
[1099] {16,27,35} 0.005479452
[1100] {16,17,36} 0.005479452
[1101] {16,34,45} 0.005479452
[1102] {11,16,44} 0.005479452
[1103] {1,10,16} 0.005479452
[1104] {7,16,40} 0.005479452
[1105] {11,21,38} 0.005479452
[1106] {1,21,29} 0.005479452
[1107] {21,24,26} 0.005479452
[1108] {21,25,36} 0.005479452
[1109] {21,26,36} 0.005479452
[1110] {11,21,45} 0.005479452
[1111] {5,21,25} 0.005479452
[1112] {15,21,34} 0.005479452
[1113] {8,21,31} 0.005479452
[1114] {21,27,31} 0.005479452
[1115] {18,21,26} 0.005479452
[1116] {11,18,21} 0.005479452
[1117] {14,18,21} 0.005479452
[1118] {11,21,43} 0.005479452
[1119] {11,17,21} 0.005479452
[1120] {8,14,21} 0.005479452
[1121] {23,29,33} 0.005479452
[1122] {23,26,39} 0.005479452
[1123] {20,23,44} 0.005479452
[1124] {6,38,39} 0.005479452
[1125] {6,7,38} 0.005479452
[1126] {6,35,40} 0.005479452
[1127] {4,6,10} 0.005479452
[1128] {6,10,37} 0.005479452
[1129] {5,6,26} 0.005479452
[1130] {6,13,40} 0.005479452
[1131] {4,5,6} 0.005479452
[1132] {24,33,38} 0.005479452
[1133] {18,31,38} 0.005479452
[1134] {7,10,38} 0.005479452
[1135] {7,38,40} 0.005479452
[1136] {18,26,38} 0.005479452
[1137] {24,26,29} 0.005479452
[1138] {27,29,36} 0.005479452
[1139] {7,29,44} 0.005479452
[1140] {7,29,43} 0.005479452
[1141] {26,29,33} 0.005479452
[1142] {11,26,29} 0.005479452
[1143] {5,29,34} 0.005479452
[1144] {1,8,29} 0.005479452
[1145] {1,17,29} 0.005479452
[1146] {24,27,35} 0.005479452
[1147] {35,36,44} 0.005479452
[1148] {33,35,36} 0.005479452
[1149] {17,35,36} 0.005479452
[1150] {35,37,45} 0.005479452
[1151] {15,26,35} 0.005479452
[1152] {20,35,44} 0.005479452
[1153] {27,33,35} 0.005479452
[1154] {20,33,35} 0.005479452
[1155] {27,35,43} 0.005479452
[1156] {20,35,43} 0.005479452
[1157] {5,27,35} 0.005479452
[1158] {5,34,35} 0.005479452
[1159] {17,20,35} 0.005479452
[1160] {4,20,35} 0.005479452
[1161] {27,35,37} 0.005479452
[1162] {3,12,20} 0.005479452
[1163] {7,12,15} 0.005479452
[1164] {12,15,34} 0.005479452
[1165] {3,24,33} 0.005479452
[1166] {7,15,24} 0.005479452
[1167] {24,33,40} 0.005479452
[1168] {5,20,24} 0.005479452
[1169] {3,11,39} 0.005479452
[1170] {3,33,37} 0.005479452
[1171] {3,11,37} 0.005479452
[1172] {3,5,20} 0.005479452
[1173] {1,3,27} 0.005479452
[1174] {1,3,20} 0.005479452
[1175] {3,20,27} 0.005479452
[1176] {25,36,39} 0.005479452
[1177] {25,33,36} 0.005479452
[1178] {14,15,36} 0.005479452
[1179] {26,31,36} 0.005479452
[1180] {8,36,39} 0.005479452
[1181] {17,36,39} 0.005479452
[1182] {27,36,39} 0.005479452
[1183] {10,36,44} 0.005479452
[1184] {14,18,36} 0.005479452
[1185] {5,34,36} 0.005479452
[1186] {10,19,45} 0.005479452
[1187] {8,19,25} 0.005479452
[1188] {19,25,34} 0.005479452
[1189] {14,15,19} 0.005479452
[1190] {15,19,34} 0.005479452
[1191] {8,19,39} 0.005479452
[1192] {10,19,40} 0.005479452
[1193] {13,18,19} 0.005479452
[1194] {14,19,43} 0.005479452
[1195] {4,8,19} 0.005479452
[1196] {8,19,34} 0.005479452
[1197] {18,31,45} 0.005479452
[1198] {13,18,45} 0.005479452
[1199] {13,33,45} 0.005479452
[1200] {37,43,45} 0.005479452
[1201] {15,25,43} 0.005479452
[1202] {17,25,39} 0.005479452
[1203] {25,27,34} 0.005479452
[1204] {14,15,18} 0.005479452
[1205] {14,15,26} 0.005479452
[1206] {13,15,34} 0.005479452
[1207] {5,10,31} 0.005479452
[1208] {10,17,31} 0.005479452
[1209] {4,31,34} 0.005479452
[1210] {14,31,40} 0.005479452
[1211] {31,37,40} 0.005479452
[1212] {8,18,39} 0.005479452
[1213] {13,14,39} 0.005479452
[1214] {8,17,39} 0.005479452
[1215] {4,8,39} 0.005479452
[1216] {8,14,39} 0.005479452
[1217] {1,8,39} 0.005479452
[1218] {10,33,44} 0.005479452
[1219] {5,34,44} 0.005479452
[1220] {17,20,44} 0.005479452
[1221] {4,10,40} 0.005479452
[1222] {1,10,40} 0.005479452
[1223] {1,10,20} 0.005479452
[1224] {7,18,20} 0.005479452
[1225] {7,20,33} 0.005479452
[1226] {5,7,20} 0.005479452
[1227] {1,7,37} 0.005479452
[1228] {7,37,40} 0.005479452
[1229] {1,8,18} 0.005479452
[1230] {26,40,43} 0.005479452
[1231] {13,26,34} 0.005479452
[1232] {11,13,33} 0.005479452
[1233] {13,33,43} 0.005479452
[1234] {11,37,43} 0.005479452
[1235] {4,11,20} 0.005479452
[1236] {13,37,43} 0.005479452
[1237] {17,27,43} 0.005479452
[1238] {5,13,14} 0.005479452
[1239] {8,17,27} 0.005479452
[1240] {4,8,27} 0.005479452
[1241] {8,27,40} 0.005479452
print(head(df[1000:10000,]))
items support
[1000] {27,29,40} 0.006849315
[1001] {35,43,45} 0.006849315
[1002] {14,35,39} 0.006849315
[1003] {20,35,40} 0.006849315
[1004] {12,15,24} 0.006849315
[1005] {4,12,24} 0.006849315
library(stringr)
df$items = str_replace(string = df$items,pattern = "\\{",replacement = "")
df$items = str_replace(string = df$items,pattern = "\\}",replacement = "")
print(head(df[1000:10000,]))
items support
[1000] 27,29,40 0.006849315
[1001] 35,43,45 0.006849315
[1002] 14,35,39 0.006849315
[1003] 20,35,40 0.006849315
[1004] 12,15,24 0.006849315
[1005] 4,12,24 0.006849315
4개 이상의 조합을 얻기 위한 지지도 하향 조정
rules2 <- apriori(trans[,-2],parameter = list(support=0.0005,target="frequent itemsets"))
summary(rules2)
Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
NA 0.1 1 none FALSE TRUE 5 5e-04 1
maxlen target ext
10 frequent itemsets FALSE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 0
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[44 item(s), 730 transaction(s)] done [0.00s].
sorting and recoding items ... [44 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 done [0.01s].
writing ... [23745 set(s)] done [0.03s].
creating S4 object ... done [0.01s].
set of 23745 itemsets
most frequent items:
40 20 27 34 37 (Other)
2360 2358 2275 2269 2261 78058
element (itemset/transaction) length distribution:sizes
1 2 3 4 5 6
44 946 8545 9675 3900 635
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 3.000 4.000 3.773 4.000 6.000
summary of quality measures:
support
Min. :0.001370
1st Qu.:0.001370
Median :0.001370
Mean :0.002478
3rd Qu.:0.001370
Max. :0.158904
includes transaction ID lists: FALSE
mining info:
data ntransactions support confidence
trans[, -2] 730 5e-04 1
inspect(sort(rules2[rules2@quality$support >= 0.001 & rules2@quality$support <= 0.0015], by = "support")[4870:4880])
items support
[1] {1,20,34} 0.001369863
[2] {20,27,37} 0.001369863
[3] {20,34,37} 0.001369863
[4] {20,34,40} 0.001369863
[5] {3,9,22,42} 0.001369863
[6] {9,11,22,42} 0.001369863
[7] {4,9,22,42} 0.001369863
[8] {9,21,22,30} 0.001369863
[9] {6,9,22,30} 0.001369863
[10] {9,22,24,30} 0.001369863
[11] {9,11,22,30} 0.001369863
로또 선택 전략
여러가지 번호를 선택하는 방향이 있을거라고 판단 된다.
**지지도 => 그 숫자 또는 숫자의 집합이 나올 확률 **
- 개별의 숫자 6개 ( 지지도 상위 6개 ) 선택
- 1 1 1 3 / 1 1 3 1 / 1 3 1 1 / 3 1 1 1
- 1 1 2 2 / 1 2 1 2 / 1 2 2 1 / 2 1 2 1 / 2 1 1 2 / 2 2 1 1 과 같은 순으로 선택
1. 개별 지지도 상위 6개 선택
- 20,40,34,27,1,37
- 확률 0.00001290….
# 개별 확률의 곱으로 전체 확률을 표현.
prob <- 1
for(i in 1:6){
#print(paste(df[i,1]," ",df[i,2]))
#print(df[i,1])
prob <- prob * df[i,2]
print(paste("cusum : ",prob))
}
print(prob)
[1] "cusum : 0.158904109589041"
[1] "cusum : 0.0248151623193845"
[1] "cusum : 0.00384125115354856"
[1] "cusum : 0.000584080654854644"
[1] "cusum : 8.72120429851455e-05"
[1] "cusum : 1.29026036197202e-05"
[1] 1.29026e-05
2. 개별 항목 3개, 3개 집합 1개 선택
- 최상위 개별 선택 3개 항목
- {20} 0.1589041
- {40} 0.1561644
- {34} 0.1547945
- 3개 항목 최상위 위의 숫자를 제외한
- {19,25,28} 0.006849315
- 20,40,34,19,25,28
- 확률 : 0.0000263
3. 3개의 집단 2개 선택
- {19,25,28} 0.006849315
- {10,16,41} 0.006849315
- 확률 : 0.000049
4 + 2 Set 조합
- {3,9,22,42} 0.001369863
- {20,35} 0.02876712
- 확률 : 0.000042
결론
- 개별 항목이 나올 확률은 비슷하게 나온다. => 즉, 특정 숫자가 많이 나오지는 않는다.
- 개별 항목이 확률이 높게 나타나더라도 조합의 갯수가 많은 숫자를 선택하는 것이 당첨될 확률이 높다.
- 현재까지 나온 숫자로 본 결과 3,3개의 조합이 가장 크게 나왔다.
- 하지만 최종 결론은 수치와 데이터를 믿고 로또를 하는 일은 수치를 다루는 사람이 아니라고 생각되며, 운과 행운을 빌어 재미삼아 하길 기원한다.
