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Generalized Sequential Pattern (GSP) Mining in Data Mining

Last Updated : 02 Feb, 2023
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GSP is a very important algorithm in data mining. It is used in sequence mining from large databases. Almost all sequence mining algorithms are basically based on a prior algorithm. GSP uses a level-wise paradigm for finding all the sequence patterns in the data. It starts with finding the frequent items of size one and then passes that as input to the next iteration of the GSP algorithm. The database is passed multiple times to this algorithm. In each iteration, GSP removes all the non-frequent itemsets. This is done based on a threshold frequency which is called support. Only those itemsets are kept whose frequency is greater than the support count. After the first pass, GSP finds all the frequent sequences of length-1 which are called 1-sequences. This makes the input to the next pass, it is the candidate for 2-sequences. At the end of this pass, GSP generates all frequent 2-sequences, which makes the input for candidate 3-sequences. The algorithm is recursively called until no more frequent itemsets are found. 

Basic of Sequential Pattern (GSP) Mining:

  • Sequence: A sequence is formally defined as the ordered set of items {s1, s2, s3, …, sn}. As the name suggests, it is the sequence of items occurring together. It can be considered as a transaction or purchased items together in a basket. 
  • Subsequence: The subset of the sequence is called a subsequence. Suppose {a, b, g, q, y, e, c} is a sequence. The subsequence of this can be {a, b, c} or {y, e}. Observe that the subsequence is not necessarily consecutive items of the sequence. From the sequences of databases, subsequences are found from which the generalized sequence patterns are found at the end.
  • Sequence pattern: A sub-sequence is called a pattern when it is found in multiple sequences. The goal of the GSP algorithm is to mine the sequence patterns from the large database. The database consists of the sequences. When a subsequence has a frequency equal to more than the “support” value.  For example: the pattern <a, b> is a sequence pattern mined from sequences {b, x, c, a}, {a, b, q}, and {a, u, b}.

         Sequential Pattern (GSP) Mining uses:

  Sequential pattern mining, also known as GSP (Generalized Sequential Pattern) mining, is a technique used to identify patterns in sequential data. The goal of GSP mining is to discover patterns in data that occur over time, such as customer buying habits, website navigation patterns, or sensor data.

Some of the main uses of GSP mining include:

Market basket analysis: GSP mining can be used to analyze customer buying habits and identify products that are frequently purchased together. This can help businesses to optimize their product placement and marketing strategies.

  1. Fraud detection: GSP mining can be used to identify patterns of behavior that are indicative of fraud, such as unusual patterns of transactions or access to sensitive data.
  2. Website navigation: GSP mining can be used to analyze website navigation patterns, such as the sequence of pages visited by users, and identify areas of the website that are frequently accessed or ignored.
  3. Sensor data analysis: GSP mining can be used to analyze sensor data, such as data from IoT devices, and identify patterns in the data that are indicative of certain conditions or states.
  4. Social media analysis:   GSP mining can be used to analyze social media data, such as posts and comments, and identify patterns in the data that indicate trends, sentiment, or other insights.
  5. Medical data analysis: GSP mining can be used to analyze medical data, such as patient records, and identify patterns in the data that are indicative of certain health conditions or trends.   

Methods for Sequential Pattern Mining:

  • Apriori-based Approaches
    • GSP
    • SPADE
  • Pattern-Growth-based Approaches
    • FreeSpan
    • PrefixSpan

Sequence Database: A database that consists of ordered elements or events is called a sequence database. Example of a sequence database:

S.No. SID sequences
1. 100 <a(ab)(ac)d(cef)> or <a{ab}{ac}d{cef}>
2. 200 <(ad)c(bcd)(abe)>
3. 300 <(ef)(ab)(def)cb>
4. 400 <eg(adf)CBC>

Transaction: The sequence consists of many elements which are called transactions.

<a(ab)(ac)d(cef)> is a sequence whereas (a), (ab), (ac), 

(d) and (cef) are the elements of the sequence. 

These elements are sometimes referred as transactions.

An element may contain a set of items. Items within an element are unordered and we list them alphabetically. 

For example, (cef) is the element and it consists of 3 items c, e and f. 

Since, all three items belong to same element, their order does not matter. But we prefer to put them in alphabetical order for convenience. 

The order of the elements of the sequence matters unlike order of items in same transaction.

k-length Sequence:  

The number of items involved in the sequence is denoted by K. A sequence of 2 items is called a 2-len sequence. While finding the 2-length candidate sequence this term comes into use. Example of 2-length sequence is: {ab}, {(ab)}, {bc} and {(bc)}.

  • {bc} denotes a 2-length sequence where b and c are two different transactions. This can also be written as {(b)(c)}
  • {(bc)} denotes a 2-length sequence where b and c are the items belonging to the same transaction, therefore enclosed in the same parenthesis. This can also be written as {(cb)}, because the order of items in the same transaction does not matter.

Support in k-length Sequence:

Support means the frequency. The number of occurrences of a given k-length sequence in the sequence database is known as the support. While finding the support the order is taken care.


Suppose we have 2 sequences in the database.

s1: <a(bc)b(cd)>

s2: <b(ab)abc(de)>

We need to find the support of {ab} and {(bc)}

Finding support of {ab}:

This is present in first sequence. 

s1: <a(bc)b(cd)>

Since, a and b belong to different elements, their order matters.

In second sequence {ab} is not found but {ba} is present. 

s2: <b(ab)abc(de)> Thus we don’t consider this.

Hence, support of {ab} is 1.

Finding support of {bc}:

Since, b and c are present in same element, their order does not matter.

s1: <a(bc)b(cd)>, first occurrence.

s2: <b(ab)abc(de)>, it seems correct, but is not. b and c are present in different elements here. So, we don’t consider it.

Hence, support of {(bc)} is 1.

How to join L1 and L1 to give C2?

L1 is the final 1-length sequence after pruning. After pruning all the entries left in the set have supported greater than the threshold.

Case 1: Join {ab} and {ac}

s1: {ab}, s2: {ac}

After removing a from s1 and c from s2.

s1’={b}, s2’={a}

s1′ and s2′ are not same, so s1 and s2 can’t be joined.

Case 2: Join {ab} and {be}

s1: {ab}, s2: {be}

After removing a from s1 and e from s2.

s1’={b}, s2’={b}

s1′ and s2′ are exactly same, so s1 and s2 be joined.

s1 + s2 = {abe}

Case 3: Join {(ab)} and {be}

s1: {(ab)}, s2: {be}

After removing a from s1 and e from s2.

s1’={(b)}, s2’={(b)}

s1′ and s2′ are exactly same, so s1 and s2 be joined.

s1 + s2 = {(ab)e}

s1 and s2 are joined in such a way that items belong to correct elements or transactions.

Pruning Phase: While building Ck (candidate set of k-length), we delete a candidate sequence that has a contiguous (k-1) subsequence whose support count is less than the minimum support (threshold). Also, delete a candidate sequence that has any subsequence without minimum support.

{abg} is a candidate sequence of C3.

{abg} is a candidate sequence of C3.

To check if {abg} is proper candidate or not, without checking its support, we check the support of its subsets.

Because subsets of 3-length sequence will be 1 and 2 length sequences. We build the candidate sets increment like 1-length, 2-length and so on.

Subsets of {abg} are: {ab], {bg} and {ag}

Check support of all three subsets. If any of them have support less than minimum support then delete the sequence {abg} from the set C3 otherwise keep it.

Challenges in  Generalized Sequential Pattern Data Mining

The database is passed many times to the algorithm recursively. The computational efforts are more to mine the frequent pattern. When the sequence database is very large and patterns to be mined are long then GSP encounters the problem in doing so effectively. 

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