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Abdullah Mueen5 Slides Demo1Primitives for Time Series Data MiningTime series motifsTime series shapeletsTime series join0200040006000800010000010203001002003004005006007008003/27/19962/24/19981/25/200012/25/200111/25/200310/25/20059/25/20078/25/20097/26/20116/26/2013-4-3-2-101234Indian Rupee (INR)Singapore Dollar (SGD)113 daysIs P within ?yesno PJoinMotifShapeletExamples of time series primitives (motifs, shapelets and joins). Such primitives are useful as features in machine learning algorithms as well as explain underlying phenomena in the data.2Phonetic Time Series MiningPhoneme ClassificationAccent Classificationgasser/G AE S ER/unattached/AH N AH T AE CH T/appreciable/AH P R IY SH AH B AH L/
cliched/K L IY SH EY D/DTW labelMFCC labelDual Domain labelDTW labelMFCC labelDual Domain labelsavagely/S AE V IH JH L IY /deactivate/D IY AE K T IH V EY T /valueless/V AE L Y UW L AH S/philosophically/F IH L AH S AA F IH K L IY /
Phonemes are small audio signals representing unit of human speech. We developed a hierarchical classification technique for phoneme classification on words collected from online dictionaries. Dual-domain labels are calculated using time domain classification in the upper level and frequency domain classification in the lower level.3Mining Heterogeneous Online ReviewsIdentifying spamming ownersCombining multi-source informationAnalytical tool for hosting sites
Negative correlation in rating distribution across hosting sites. Georgia hotels show significantly more negative correlations than Nevada hotels.4
Mining Motion DataClassifying motion categoryInvariant to plane and length of motion
010203040506070-4-202010203040506070-4-202Walking on Carpet (Soft)Walking on Cement (Hard)010203040506070-4-2024S1S21-NN69.55%1-NN DTW72.55%1-NN DTW S-C69.55%Shapelet (S1)93.34%Shapelets (S1 and S2 )96.34%
x 104ABCEFAAABCCDDDDDDEEFF00.511.522.530/20/22/40/30/40/2HandLegxyzxyzBottom: The dance steps a subject performed on a Lady Gaga song. The choreography shown at the top in the middle figure. The repeating patterns that our method detected are shown in colored patches and the number of times the repetitions were from different steps are shown right of each accelerometer signals.
Right: Accelerometer signals from the foot of a Sony robot walking on carpet and cement. The shapes we found to be distinguishing gives us maximum classification accuracy.5Contextual Change Detection0102030405060708090100-20-15-10-50510150102030405060708090100-20-15-10-5050102030405060708090100-20-15-10-505101520DisbandingFormation-20-15-10-505010203040506070809010025-May-200725-May-200825-May-200925-May-201025-May-201125-May-20120.050.150.250.35
Contextual ChangeFigure: A set of EVI time series which disbands in August 2009 because of forest fire. Such disbanding pattern is useful to detect events from time series datasets. Red and green points show the time series of points inside (marked as the red locations) and outside (marked as the green locations) the fire a affected region, respectively.