Hardware And Software Requirements

HardWare:512MB RAM

Software Requirements:Java Run Time Environment

Database –IBM db2

Literature Survey

1. Sequencial Pattern1.1. Priori Based Methods

1.1.1. Apriori All1.1.2. Apriori Some1.1.3. Gsp1.1.4. Spirit 1.1.5.Spade

1.1.5.1. Breadth First Search

1.1.5.2. Depth first search

1.6. Pattern Growth method1.2. Free span1.3. Prefix Span

2. Fuzzy set theory3. Fuzzy Sequencial Patterns

Literature Survey

• Apriori All• Spirit• Spade

Breadth First search Depth First search

• Pattern Growh method• FreeSpan• PrefixSpan

Literature Survey

• Apriori All vs Apriori Some:As AprioriSome is concentrated into

maximal sequences and skipping the smaler sequences which cannot be the solution.

Apriori Algorithm

Apriori Some AlgorithmForward Phase

Apriorisome AlgorithmForward 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

Apriori Some AlgorithmForward Phase

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

Modules

• Finding Fuzzy sequence

• Applying K-means Clustering

• Using Apriori Some Algorithm finding the frequent pattern

Steps for finding the fuzzy sets from the database

• Use the clustering techniques (K-means algorithm).

• Determine the membership function.

Apriori Some AlgorithmPhase 1

Sort The Database

Transforms the QuantitativeValues into fuzzy sets

Find the all large Fuzzy sequence

Apriori Some AlgorithmPhase 2

Forward Phase

Count the Support count

Backward Phase

AprioriSome Algorithm

Less than Threshold

Larger Than

Threshold

Find the Desired Sequence

End

Fuzzy sequential patterns mining with AprioriSome algorithm

1. Analyse the transaction Data.

2. Count each scalar cardinality of each region Rk in the transaction (count jl).

3. For each Pjl, checkwhether countjl is larger than or equal tothe predefined minimum support for each Pjl, j =1–m, l =1–k.

4. If Pjl satisfies this condition, then it is a large 1-fuzzy sequence. L1={Pjl |Count jl>= , j=1-m, l=1-k).

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 andrepeat Step 5.

8. In this step, we use AprioriSome algorithm to produce thedesired patterns so that we can decide which candidate should orshould not be generated by the next function. First of all, generate