View of A BRIEF STUDY ON ALL SEQUENTIAL PATTERNS USING TIME EFFICIENT TECHNIQUE

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ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal (International Journal) ISSN-2456-1037

Vol.04,Special Issue 05, (ICIR-2019) September 2019, Available Online: www.ajeee.co.in/index.php/AJEEE

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A BRIEF STUDY ON ALL SEQUENTIAL PATTERNS USING TIME EFFICIENT TECHNIQUE

Sujit R. Wakchaure

Dept. of Computer Science and Technology, AKU University, Indore

Abstract - The database utilized in the mining process typically contains massive amounts of information collected by computerized applications. We can't mention the natively computing-centric environments like web access logs in net applications. These databases further used as rich and reliable sources for data generation and verification. Meanwhile, the massive databases present challenges for effective approaches for knowledge discovery.

We might categorize recent studies in frequent pattern mining into the invention of association rules and also the discovery of sequential patterns. Association discovery finds closely correlated sets in order that the presence of some elements during a frequent set will imply the presence of the remaining elements (in identical set). Sequential pattern discovery finds temporal associations in order that not solely closely correlated sets however additionally their relationships in time are uncovered. Mining of sequential patterns is to get all sequences with a user such least support. The objective of sequential patterns is to look out and to search the sequences that have bigger than or equivalent to an explicit user pre-specified support. In this paper we are studying some technique by which early candidate sequence pruning and search space partitioning will be feasible for effective mining of patterns likewise it will lessen the time utilization and space utilization for sequential pattern mining.

Index Terms : Sequential patterns, knowledge discovery, support and threshold, candidate sequence, projection technique.

I. INTRODUCTION

he discovered knowledge can be utilized from numerous points of view in relating applications. For instance, distinguishing the frequently appeared sets of things in a retail database can be utilized to improve the decision making of product arrangement or deals advancement. Discovering patterns of customer browsing and purchasing (from either client records or Web traversals) may help the modeling of client practices for customer retention or personalized services.

Given the ideal databases, whether relational, transactional, spatial, temporal, or multimedia ones, we may acquire helpful data after knowledge discovery process if suitable mining methods are utilized.

Data Mining Models:

The data mining models are of two kinds:

Predictive and Descriptive. The predictive model makes prediction about unknown information esteems by utilizing the known values. Ex. Order, Regression, Time series analysis, Prediction and so forth. The descriptive model recognizes the patterns or relationships in data and investigates the properties of the data inspected. Ex.

Clustering, Summarization, Association rule, Sequence discovery etc.

The proportion of frequent patterns is a user-specified threshold that shows the minimum occurring frequency of the pattern. We may classify recent studies in

frequent pattern mining into the discovery

of association rules and the discovery of sequential patterns. Association discovery discovers firmly correlated sets so that the presence of some elements in a frequent set will infer the presence of the rest elements (in a similar set). Sequential pattern discovers temporal associations so that firmly closely correlated sets as well as their relationships and connections in time are revealed. If the collection of data sequences is given, inside which each sequence might be a list of transactions requested by the transaction time, the matter of mining sequential patterns is to get all sequences with a client such minimum support. Each transaction contains a collection of things. A sequential pattern is an arranged list and rundown (sequence) of item sets.

The item sets that area unit contained inside the sequence region unit alluded to as parts of the sequence. For a given database D that comprises of customer transactions each every group action comprises of the subsequent fields: client ID, transaction time, and along these lines the things purchased inside the group action. An item-set might be a non-empty set of things, and a sequence is is an order list of item-sets. We are stating a sequence A is contained in another sequence B if there exist integer numbers i1.

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2 II. LITERATURESURVEY

By and large, we may categorize the mining approaches into the generate-and-test framework and the pattern-growth one, for sequence databases of horizontal layout.

Typifying the previous methodologies [1, 2, 3] the GSP (Generalized Sequential Pattern) algorithm [3] creates potential patterns (called candidates), filters every data sequence in the database to figure the frequencies of candidates (called supports), and afterward recognizes candidates having enough supports as sequential patterns.

The sequential patterns in current database pass become seeds for creating candidates in the next pass. This generate and-test procedure is rehashed until not any more new candidates are produced. At the point when candidates can't fit in memory in a cluster, GSP re-examines the database to test the rest of the candidates that have not been stacked into memory.

Therefore, GSP scans at k times of the on- circle database if the most extreme size of the discovered patterns is k, which causes high cost of disk perusing. In spite of that GSP was great at candidate pruning, the quantity of candidates is still gigantic that may impede the mining proficiency.

The Prefix Span (Prefix-projected Sequential pattern mining) algorithm [4], speaking to the pattern-growth methodology [5, 4, 6], finds the frequent items in the wake of scanning the sequence database once. The database is then anticipated, as indicated by the frequent items, into a few smaller databases. At last, the complete set of sequential patterns is found by recursively developing subsequence pieces in each projected database. Two enhancements for minimizing disk projections were portrayed in [4]. The bi- level projection strategy, managing gigantic databases, scans each data sequence twice in the (projected) database with the goal that less and littler projected databases are produced. The pseudo-projection method, avoid physical projections, keeps up the sequence-postfix of each data sequence in a projection by a pointer-offset pair. Be that as it may, as indicated by [4], maximum mining execution can be accomplished just when the database size is decreased to the size accommodable by the main memory by utilizing pseudo-projection in the wake of utilizing bi-level optimization. Despite the fact that PrefixSpan effectively found patterns employing the divide-and-conquer

strategy, the cost of disk I/O may be high because of the creation and handling of the projected sub-databases.

Other than the horizontal layout, the sequence database can be changed into a vertical format comprising of items' id- records [7, 8, 9]. The id-list of a item is a list of (sequence-id, timestamp) sets indicating the occuring timestamps of the items in that sequence. Looking in the lattice formed by id-list intersections, the SPADE (Sequential Pattern Discovery using Equivalence classes) algorithm [9] finished the mining in three passes of database scanning. By the by, extra computation time is required to transform a database of horizontal layout to vertical format, which likewise requires extra storage a several times bigger than that of the original sequence database.

With fast cost down and the proof of the increased in installed memory size, numerous little or medium estimated databases will fit into the main memory. For instance in a example, a platform with 256MB memory may hold a database with one million sequences of absolute size 189MB. Pattern mining performed straightforwardly in memory presently winds up conceivable. In any case, current methodologies discover the patterns either through multiple scans of the database or by iterative database projections, subsequently requiring bounteous disk operations. The mining efficiency could be improved if the excessive disk I/O is decreased by upgrading memory use in the discovering process.

So as to reduce the number of iterations, a efficient bidirectional sequential pattern mining approach in particular Recursive Prefix Suffix Pattern detection, RPSP [12]

algorithm is proposed. The RPSP algorithm first discovers all Frequent Item sets (FI’’s) as per the given minimum support and changes the database to such an extent that every transaction is replaced by all the FI’’s it contains and afterward finds the patterns.

The pattern further is detected based on ith projected databases, and develops suffix and prefix databases dependent on the apriori property. RPSP will expand the quantity of frequent patterns by reducing the minimum support and the other way around. Recursion is ended when the identified FI set of prefix or postfix projected database of parent database is null. Every one of the patterns that correspond to a specific i^th projected database of transformed database are framed into a set,

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3 which is disjoint from every other set. The union of all the disjoint subsets is the resultant set of frequent patterns. The proposed algorithm was tried and tested on the speculative data and results acquired were found satisfactory. In this way, RPSP algorithm can be appropriate to numerous real world sequential data sets.

Prefix Span algorithm speaks to another pattern-growth approach for mining sequential patterns. The algorithm suggests that the projection of sequence databases depends just on occurrence of frequent prefixes and not by considering all the possible occurrences of frequent subsequences since any frequent subsequence can be found by developing its frequent prefix [13]. The PrefixSpan algorithm scans just the prefix subsequences and projects their relating postfix subsequences into the databases.

Along these lines, sequential patterns in each projected database develop by exploration. The steps of algorithm are given as follows [13]: The highlights of updated Prefix Span can be condensed as follows: i.

No candidate sequence should be created by updated Prefix Span. ii. A projected database is littler than the original database since in it just the postfix subsequences of a frequent prefix are projected. In this manner, the projected databases keep on shrinking. In the most pessimistic scenario, updated PrefixSpan develops a projected database for each sequential pattern.

III. SEQUENTIAL PATTERNS SCENARIO ANDANALYSIS

Sequential patterns analysis is one of data mining technique that looks to find comparative and similar patterns in data transaction over a business period. The reveal or uncover patterns are utilized for further business analysis to perceive connections among data.

Sequential example mining is attempting to discover the relationships between occurrences of sequential events by searching for a particular order of occurrences. At the end of the day, sequential pattern mining is targeting finding the as often as possible or frequent occurred sequences to describe the data or predict future data or mining periodical patterns.

Sequential example is a sequence of element sets that occurs frequently in particular order, all items in a similar item sets should have a similar transaction-time

value or inside a time gap [14]. Sequential example mining can be isolated into two sections as indicated by the route by which candidate sequences are generated and stored. What's more, the manner by which support is counted and how candidate sequences are tested for frequency.

Fig. 1. Structure of sequential pattern mining

Consider the database [14] as our problem is to find all frequent sequences, given min_sup=2.

Table.1. Sequence in database

In table 1 there is a database having Seq ID and sequences. On the basis of min support which is 2 filter the data as in table2.

Table.2. Support in database Table.3. Candidate generation

As shown in Table 3, using Apriori one needs to generate just 51 length-2 candidates, while without Apriori property,

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4 8*8+8*7/2=92 candidates would need to be generated. For this example, Apriori would perform 5 database scans, pruning away candidates with support less than min sup.

Candidates that cannot pass support threshold are pruned.

1stscan: 8 candidates. 6 length-1 sequence patterns.

2ndscan: 51 candidates. 19 length-2 sequence patterns. 10 candidates not in DB at all

3rdscan: 46 candidates. 19 length-3 sequence patterns. 20 candidates not in DB at all

4thscan: 8 candidates. 6 length-4 sequence patterns.

5thscan: 1 candidate. 1 length-5 sequence patterns.

IV. CONCLUSION

The goal of sequential patterns is to look out the sequences that have bigger than or equivalent to an explicit user pre-specified support. Once in a while the technique for discovering sequential patterns comprises of the subsequent sections: sorting phase, finding the massive item-set phase, transformation section, sequence section, and greatest phase. In this paper concentrate done on audit of every cutting edge and modern strategy for mining sequential patterns from a big data set classification of sequential pattern mining techniques. After analyzing all modern sequential pattern mining strategy, work will provide a classification of sequential pattern mining techniques with studied research gap, we will device an effective algorithm to mine all the sequential pattern from a big data set.

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