Week 5 Video 4 Relationship Mining Sequential Pattern Mining

Association Rule Mining ¨ Try to automatically find if-then rules within the data set

Sequential Pattern Mining ¨ Try to automatically find temporal patterns within the data set

ARM Example ¨ If person X buys diapers, ¨ Person X buys beer ¨ Purchases occur at the same time

SPM Example ¨ If person X takes Intro Stats now, ¨ Person X takes Advanced Data Mining in a later semester ¨ Conclusion: recommend Advanced Data Mining to students who have previously taken Intro Stats ¨ Doesn’t matter if they take other courses in between

SPM Example ¨ Learners in virtual environments have different sequences of behavior depending on their degree of self-regulated learning ¨ High self-regulated learning: Tend to gather information and then immediately record it carefully ¨ Low self-regulated learning: Tend to gather more information without pausing to record it (Sabourin, Mott, & Lester, 2011)

Different Constraints than ARM ¨ If-then elements do not need to occur in the same data point ¨ Instead ¤ If-then elements should involve the same student (or other organizing variable, like teacher or school) ¤ If elements can be within a certain time window of each other ¤ Then element time should be within a certain window after if times

Sequential Pattern Mining ¨ Find all subsequences in data with high support ¨ Support calculated as number of sequences that contain subsequence, divided by total number of sequences

GSP (Generalized Sequential Pattern) ¨ Classic Algorithm for SPM ¨ (Srikant & Agrawal, 1996)

Data pre-processing ¨ Data transformed from individual actions to sequences by user ¨ Bob: {GAMING and BORED, OFF-TASK and BORED, ON-TASK and BORED, GAMING and BORED, GAMING and FRUSTRATED, ON-TASK and BORED}

Data pre-processing ¨ In some cases, time also included ¨ Bob: {GAMING and BORED 5:05:20, OFF-TASK and BORED 5:05:40, ON-TASK and BORED 5:06:00, GAMING and BORED 5:06:20, GAMING and FRUSTRATED 5:06:40, ON-TASK and BORED 5:07:00}

Algorithm ¨ Take the whole set of sequences of length 1 ¤ May include “ANDed” combinations at same time ¨ Find which sequences of length 1 have support over pre-chosen threshold ¨ Compose potential sequences out of pairs of sequences of length 1 with acceptable support ¨ Find which sequences of length 2 have support over pre-chosen threshold ¨ Compose potential sequences out of triplets of sequences of length 1 and 2 with acceptable support ¨ Continue until no new sequences found

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a , ab c , ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a , abc, a c , de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a , abc, ac, de, c ef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, a bc, a c , de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, a bc, ac, de, c ef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, a c, de, c ef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: a f, ab, a c d, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: a f, ab, acd, dab c , ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, a b, a c d, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, a b, acd, dab c , ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, a cd, dab c , ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: a ef, ab, a c eh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, a b, a c eh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a , b c , acf, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a , bc, a c f, d, abeg a, b, c, d, e, f, ac

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac (14/40=35%)

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a , a bc, ac, d e, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae, aad ,

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a , abc, a c, d e, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae, aad

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: a f, a b, ac d , dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae, aad

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: a f, a b, acd, d abc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae, aad

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: a f, ab, a cd, d abc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae, aad

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: a ef, a b, aceh, d , ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae, aad

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: a ef, ab, a ceh, d , ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae, aad

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a , bc, a cf, d , abeg a, b, c, d, e, f, ac, ad, ae, aad

Let’s execute GPS algorithm ¨ With min support = 20% ¨ Chuck: a, abc, ac, de, cef ¨ Darlene: af, ab, acd, dabc, ef ¨ Egoberto: aef, ab, aceh, d, ae ¨ Francine: a, bc, acf, d, abeg a, b, c, d, e, f, ac, ad, ae, aad, aae, ade

Let’s execute GPS algorithm ¨ From ¨ ac, ad, ae, aad, aae, ade ¨ To ¨ a à c, a à d, a à e, a à ad, a à ae, ad à e

Other algorithms ¨ Free-Span ¨ Prefix-Span ¨ Select sub-sets of data to search within ¨ Faster, but same basic idea as in GPS

Differential Sequence Mining (Kinnebrew et al., 2013) ¨ Compares the support for sequential patterns between two groups ¨ Such as high-performing and low-performing students ¨ To find the patterns that are much more common in one group than the other

Week 5 Video 4 Relationship Mining Sequential Pattern Mining - PowerPoint PPT Presentation

Week 5 Video 4 Relationship Mining Sequential Pattern Mining Association Rule Mining Try to automatically find if-then rules within the data set Sequential Pattern Mining Try to automatically find temporal patterns within the data set

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