Kmeans
- 1. Hardware And Software Requirements HardWare: 512MB RAM Software Requirements: Java Run Time Environment Database –IBM db2
- 2. Literature Survey 1. Sequencial Pattern 1.1. Priori Based Methods 1.1.1. Apriori All 1.1.2. Apriori Some 1.1.3. Gsp 1.1.4. Spirit 1.1.5.Spade 1.1.5.1. Breadth First Search 1.1.5.2. Depth first search 1.6. Pattern Growth method 1.2. Free span 1.3. Prefix Span 2. Fuzzy set theory 3. Fuzzy Sequencial Patterns
- 3. Literature Survey Apriori All Spirit Spade Breadth First search Depth First search Pattern Growh method FreeSpan PrefixSpan
- 4. Literature Survey Apriori All vs Apriori Some: As AprioriSome is concentrated into maximal sequences and skipping the smaler sequences which cannot be the solution.
- 6. Apriori Some Algorithm Forward Phase
- 7. Apriorisome Algorithm Forward Phase First generate the first candidate set For each of the itemset, calculate the fsupp value. Check if the fuzzy support is larger than the threshold then go to forward phase Else go to backward phase Forward phase Apply join and prune step
- 8. Apriori Some Algorithm Forward Phase
- 9. Apriori Some Algorithm Backward Phase Backward Phase: For all lengths which we skipped: Delete sequences in candidate set which are contained in some large sequence. Count remaining candidates and find all sequences with min. support. Also delete large sequences found in forward phase which are non-maximal. Apply both these steps untill no more frequent itemset can be generated
- 10. Modules Finding Fuzzy sequence Applying K-means Clustering Using Apriori Some Algorithm finding the frequent pattern
- 11. Steps for finding the fuzzy sets from the database Use the clustering techniques (K-means algorithm ). Determine the membership function.
- 12. Apriori Some Algorithm Phase 1 Sort The Database Transforms the Quantitative Values into fuzzy sets Find the all large Fuzzy sequence
- 13. Apriori Some Algorithm Phase 2 Forward Phase Count the Support count Backward Phase AprioriSome Algorithm Less than Threshold Larger Than Threshold Find the Desired Sequence End
- 14. Fuzzy sequential patterns mining with AprioriSome algorithm Analyse the transaction Data. Count each scalar cardinality of each region Rk in the transaction (count jl). For each Pjl, checkwhether countjl is larger than or equal tothe predefined minimum support for each Pjl, j =1–m, l =1–k. If Pjl satisfies this condition, then it is a large 1-fuzzy sequence. L1={Pjl |Count jl>= , j=1-m, l =1-k).
- 15. Fuzzy sequential patterns mining with AprioriSome algorithm 5. Then we need to find the large k-fuzzy sequence. Set r =1. 6. Generate the candidate set Cr from Lr=1. 7. Implement the next step until Lr is null, or set r = r - 1 and repeat Step 5. 8. In this step, we use AprioriSome algorithm to produce the desired patterns so that we can decide which candidate should or should not be generated by the next function. First of all, generate