S - UWSpace - University of Waterloo

More documents

Recommendations

Info

$uWaterloo LaTeX Thesis Template - UWSpace - University of Waterloo$

itemsets mining. A task-specific stream mining technique is proposed. Mining frequent itemsets in dynamic streams are particularly difficult because infrequent itemsets are usually not maintained due to limited memory. However, when a distribution change occurs, a once infrequent itemset may become frequent under the new distribution. Such new frequent itemsets are difficult to detect. Furthermore, even if these itemsets can be detected, their statistics can not be obtained, since mining a data stream is a one-pass procedure and historical information is irretrievable. In Chapter 5, a false-negative oriented algorithm is proposed that can find most of the frequent itemsets, detect distribution changes, and update the mining results accordingly. This technique uses two windows (window models will be discussed in Section 2.1.2), one for maintaining existing frequent itemsets and one for predicting new frequent itemsets. A candidate list is maintained that contains a list of itemsets that have potential to become frequent. Every time the two windows move, we check if new frequent itemsets and candidates should be added and if some existing ones need to be removed from the list. The contribution of this thesis can be briefly summarized as follows: • The issue of mining time-changing data streams is systematically studied. Data stream mining has only gained popularity recently, and many proposed techniques are based on the false-assumption that distribution in the data stream will always be static. In this thesis, we study this important but challenging issue both in depth and in width. • Two generalized change detection algorithms are proposed. As has been previously discussed, most of the change detection techniques proposed in literature are task-specific. Generalized approaches are rare, but have greater potential since they can be applied to any type of stream and plugged in any stream mining applications. • These proposed techniques are extended to data streams with higher dimensions. Multi-dimensional data mining is a difficult task even for traditional data mining. Few studies have been conducted for detecting changes and mining multi-dimensional data streams. 16
• A novel technique for mining frequent itemsets in data streams is proposed. The proposed technique has the ability of detecting distribution changes in real-time, and can out-perform others according to the experiments. 1.5 Thesis Outline The remainder of this thesis is organized as follows. Chapter 2 presents the background of data stream mining. In Chapter 3, the problem of distribution change detection is discussed and two change detection techniques are proposed. In Chapter 4, we discuss the potential of extending our proposed techniques to higher dimensions. A novel approach for mining frequent itemsets in transactional streams is presented in Chapter 5. Finally, Chapter 6 concludes this thesis with summarizations and suggestions for future work. 17
Page 1 and 2: Mining Time-Changing Data Streams b
Page 3 and 4: Abstract Streaming data have gained
Page 5 and 6: Contents List of Figures ix List of
Page 7 and 8: 4 Change Detection in Multi-dimensi
Page 9 and 10: List of Figures 1.1 Abstract archit
Page 11 and 12: 3.30 Max duration for detecting tru
Page 13 and 14: 3.62 Max duration for detecting tru
Page 15 and 16: 3.96 Mean duration for detecting tr
Page 17 and 18: 3.129Number of true changes detecte
Page 19: List of Tables 3.1 Stream types gen
Page 22 and 23: I, Ii set of items for transactiona
Page 24 and 25: • Financial and market activities
Page 26 and 27: Figure 1.1: Abstract architecture o
Page 28 and 29: change detection and incremental ma
Page 30 and 31: preliminary analysis over the data
Page 32 and 33: streams is a continuous process and
Page 34 and 35: these approaches may achieve high p
Page 36 and 37: Under the assumption that attribute
Page 41 and 42: Chapter 2 Background This chapter s
Page 43 and 44: • Due to performance and storage
Page 45 and 46: S t s i s i+1 s i s i+j W W W W’
Page 47 and 48: Figure 2.3: Example of continuous d
Page 49 and 50: samples so that it can be stored in
Page 51 and 52: Change detection As discussed in Se
Page 53 and 54: Frequency counting and association
Page 55: Integration of stream mining system
Page 58 and 59: Only a few distribution change-dete
Page 60 and 61: method is inclined to be conservati
Page 62 and 63: the distribution change cannot refl
Page 64 and 65: that the probability of a value tha
Page 66 and 67: |Wr| from Wr + Wt, such that: Discr
Page 68 and 69: To partition (Wr + Wt) into a numbe
Page 70 and 71: tings, an experiment framework is d
Page 72 and 73: Table 3.1: Stream types generated S
Page 74 and 75: By combining the three window movin
Page 76 and 77: • Standard deviation of durations
Page 78 and 79: Figure 3.3: Number of true changes
Page 80 and 81: 0.00 0.02 0.04 0.06 0.08 0.10 0.12
Page 82 and 83: Figure 3.7: Number of false changes
Page 84 and 85: Figure 3.9: Mean duration for detec
Page 86 and 87: Figure 3.11: Mean duration for dete
Page 88 and 89:
Figure 3.13: Standard deviation of
Page 90 and 91:
0.0000 0.0004 0.0008 0.0012 0.00 0.
Page 92 and 93:
0.000 0.002 0.004 0.006 0.000 0.005
Page 94 and 95:
Figure 3.18: Number of true changes
Page 96 and 97:
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Page 98 and 99:
Figure 3.22: Number of false change
Page 100 and 101:
Figure 3.24: Mean duration for dete
Page 102 and 103:
Figure 3.26: Mean duration for dete
Page 104 and 105:
Figure 3.28: Standard deviation of
Page 106 and 107:
0.0000 0.0004 0.0008 0.0012 0.00 0.
Page 108 and 109:
0.000 0.005 0.010 0.015 0.020 0.000
Page 110 and 111:
than fixed window method. Fixed win
Page 112 and 113:
0.00 0.05 0.10 0.15 0.00 0.02 0.04
Page 114 and 115:
0.00 0.05 0.10 0.15 0.00 0.02 0.04
Page 116 and 117:
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Page 118 and 119:
0.000 0.010 0.020 0.030 0.000 0.004
Page 120 and 121:
0.000 0.001 0.002 0.003 0.004 0.005
Page 122 and 123:
0.000 0.002 0.004 0.006 0.008 0.000
Page 124 and 125:
0.000 0.001 0.002 0.003 0.004 0e+00
Page 126 and 127:
The following observations can be m
Page 128 and 129:
technique with smaller τ setting o
Page 130 and 131:
0.00 0.05 0.10 0.15 0.00 0.01 0.02
Page 132 and 133:
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Page 134 and 135:
0.00 0.05 0.10 0.15 0.00 0.05 0.10
Page 136 and 137:
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Page 138 and 139:
0.00 0.01 0.02 0.03 0.04 0.05 0.06
Page 140 and 141:
0.00 0.05 0.10 0.15 0.00 0.01 0.02
Page 142 and 143:
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Page 144 and 145:
These results show that larger τ v
Page 146 and 147:
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Page 148 and 149:
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Page 150 and 151:
0.00 0.02 0.04 0.06 0.08 0.10 0.00
Page 152 and 153:
0.00 0.01 0.02 0.03 0.04 0.000 0.00
Page 154 and 155:
0.000 0.005 0.010 0.015 0.020 0.025
Page 156 and 157:
0.00 0.02 0.04 0.06 0.08 0.10 0.00
Page 158 and 159:
0.00 0.02 0.04 0.06 0.08 0.10 0.00
Page 160 and 161:
3.3.7 Mining streams with periodica
Page 162 and 163:
Choosing important distributions Fo
Page 164 and 165:
containing the representative sets
Page 166 and 167:
an important feature for detecting
Page 168 and 169:
that have arrived in S in the last
Page 170 and 171:
significance level τ. Tables in [9
Page 172 and 173:
Full algorithm The full algorithm o
Page 174 and 175:
values of the change detection dura
Page 176 and 177:
Stream 3 − Exponential Distributi
Page 178 and 179:
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Page 180 and 181:
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Page 182 and 183:
Page 184 and 185:
0.00 0.02 0.04 0.06 0.08 0.10 0.00
Page 186 and 187:
0.00 0.02 0.04 0.06 0.08 0.10 Strea
Page 188 and 189:
Page 190 and 191:
0.00 0.01 0.02 0.03 0.04 0.00 0.02
Page 192 and 193:
0.00 0.02 0.04 0.06 0.00 0.01 0.02
Page 194 and 195:
Page 196 and 197:
0.000 0.002 0.004 0.006 0.008 0.010
Page 198 and 199:
0.00 0.02 0.04 0.06 0.08 0.10 0.000
Page 200 and 201:
Page 202 and 203:
0.0000 0.0004 0.0008 0.0012 0.00 0.
Page 204 and 205:
0.000 0.005 0.010 0.015 0.020 0.000
Page 206 and 207:
0.00 0.02 0.04 0.06 0.08 0.10 0.00
Page 208 and 209:
0.00 0.05 0.10 0.15 0.00 0.02 0.04
Page 210 and 211:
0.00 0.02 0.04 0.06 0.08 0.00 0.02
Page 212 and 213:
0.000 0.004 0.008 0.012 0.000 0.005
Page 214 and 215:
0.000 0.005 0.010 0.015 0.020 0.000
Page 216 and 217:
0.00 0.02 0.04 0.06 0.000 0.002 0.0
Page 218 and 219:
0e+00 2e−04 4e−04 6e−04 8e−
Page 220 and 221:
0e+00 2e−04 4e−04 6e−04 0e+00
Page 222 and 223:
0.00 0.05 0.10 0.15 0.00 0.02 0.04
Page 224 and 225:
0.00 0.02 0.04 0.06 0.08 0.000 0.00
Page 226 and 227:
0.000 0.005 0.010 0.015 0.020 0e+00
Page 228 and 229:
0.00 0.05 0.10 0.15 0.00 0.05 0.10
Page 230 and 231:
0.00 0.02 0.04 0.06 0.000 0.004 0.0
Page 232 and 233:
0.0000 0.0010 0.0020 0.0030 0e+00 2
Page 235 and 236:
Chapter 4 Change Detection in Multi
Page 237 and 238:
Recall that the original approach i
Page 239 and 240:
4.3.1 Building the multi-dimensiona
Page 241 and 242:
4.3.2 Detecting changes When data f
Page 243 and 244:
Algorithm 2 Mean Change Detection f
Page 245 and 246:
For each stream type, there are a t
Page 247 and 248:
0.0 0.1 0.2 0.3 Num of False Change
Page 249 and 250:
0.00 0.01 0.02 0.03 0.04 0.05 0.06
Page 251 and 252:
0.00 0.05 0.10 0.15 0.20 0.25 SD of
Page 253 and 254:
0.00 0.02 0.04 0.06 0.08 0.10 Mean
Page 255 and 256:
4.5.2 Performance comparison with o
Page 257 and 258:
0.00 0.02 0.04 0.06 0.08 0.10 Mean
Page 259:
Note that KD is a generic approach
Page 262 and 263:
items is too large for memory-inten
Page 264 and 265:
time t and a set of items I = {a, b
Page 266 and 267:
than three) [163]. When the sizes o
Page 268 and 269:
according to certain criteria (Sect
Page 270 and 271:
When the end of WM is reached, it t
Page 272 and 273:
Similarly, if one infrequent item r
Page 274 and 275:
the new frequent itemset with all c
Page 276 and 277:
Step 2.5. No new frequent itemset d
Page 278 and 279:
Updating candidate support For any
Page 280 and 281:
Algorithm 5 UPDATE CANDIDATE SUP 1:
Page 282 and 283:
esult at time t1 may not be consist
Page 284 and 285:
The sizes of the tumbling windows u
Page 286 and 287:
Table 5.2: Results for varying ν v
Page 288 and 289:
Table 5.5: Results for varying λ o
Page 290 and 291:
5.5.5 Memory usage The major memory
Page 292 and 293:
5.6 Summary Mining frequent itemset
Page 294 and 295:
Two techniques are proposed in this
Page 296 and 297:
change detection and mining process
Page 299 and 300:
Bibliography [1] G. Abdulla, T. Cri
Page 301 and 302:
[19] S. Chandrasekaran and M. Frank
Page 303 and 304:
[39] F. David. The moments of the z
Page 305 and 306:
[60] X. Gu, S. Papadimitriou, S. Yu
Page 307 and 308:
[81] J. Kramer and B. Seeger. Seman
Page 309 and 310:
[102] B. Park et al. Reservoir-base
Page 311 and 312:
[124] T. Dasu et al. An information
Page 313 and 314:
[144] W. Shewhart. Statistical Meth
Page 315:
[166] J. Yu, Z. Chong, H. Lu, and A
show all

S - UWSpace - University of Waterloo

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?