Common Data Mining Algorithm

1.Apriori Algorithm

This algorithm is used for mining transaction database. It can be obtaned knowledge about what items are bought at same time by a customer. I follow http://www.mathcs.emory.edu/~cheung/Courses/584-StreamDB/Syllabus/10-Mining/Apriori.html to speed up algorithm

Apriori Algorithm consist of 3 phase:

1. Generate candidate set Ck
2. Counting each set c in Ck on transitions T
3. Remove set c not bigger than supmin

Phase 1 : With phase 1 we can Brute Force generate all possible candidate with : add any item haven't been in set. -> Gen big candidate But that way is slow, I have gennerate with self-join of Ck-1: F1, F2 in Ck-1, F1 can join F1 if and only if first k-2 item of F1 and F2 are same.

Phase 2: We can loop all possible transaction and all possible candidate and increase counter when see each candidate in each transaction but it need so much computation. So we can use hash-tree to save candidate in hash-tree and then generate k-subsets of transaction, from that use each subsets to increase counter of candidate sets

Step 1: Build sub-transaction of origin transaction.

Step 2: Build hash-tree include Candidate set.

Step 3: Iterating over all sub-transaction on hash-tree, and increate counter of canidate sub-transaction visit.

2. KNN Algorithm

Step 1: Determining number K (number of neighbours we will evaluate), data D (feature and label).

Step 2: Find Similar measure on of test data on each training data, find K sample of training set has largest similar.

Step 3: Find Most appearance label on K label and assign that label for test sample.

Advantage:

1. Simple, don't have to train
2. Have no assumption about class distribution

Disadvantage:

1. Don't have train so all computation phase in test -> test phase is very slow
2. Easy to effect by noise data
3. Have to save all data train on memory

3. Decision Tree - ID3

4. K-Means

5. HAC

6. Bayes Naive