This document summarizes four algorithms for sequential pattern mining: GSP, ISM, FreeSpan, and PrefixSpan. GSP is an Apriori-based algorithm that incorporates time constraints. ISM extends SPADE to incrementally update patterns after database changes. FreeSpan uses frequent items to recursively project databases and grow subsequences. PrefixSpan also uses projection but claims to not require candidate generation. It recursively projects databases based on short prefix patterns. The document concludes by stating the goal was to find an efficient scheme for extracting sequential patterns from transactional datasets.
PERFORMANCE EVALUATION OF SQL AND NOSQL DATABASE MANAGEMENT SYSTEMS IN A CLUSTERijdms
In this study, we evaluate the performance of SQL and NoSQL database management systems namely;
Cassandra, CouchDB, MongoDB, PostgreSQL, and RethinkDB. We use a cluster of four nodes to run the
database systems, with external load generators.The evaluation is conducted using data from Telenor
Sverige, a telecommunication company that operates in Sweden. The experiments are conducted using
three datasets of different sizes.The write throughput and latency as well as the read throughput and
latency are evaluated for four queries; namely distance query, k-nearest neighbour query, range query, and
region query. For write operations Cassandra has the highest throughput when multiple nodes are used,
whereas PostgreSQL has the lowest latency and the highest throughput for a single node. For read
operations MongoDB has the lowest latency for all queries. However, Cassandra has the highest
throughput for reads. The throughput decreasesas the dataset size increases for both write and read, for
both sequential as well as random order access. However, this decrease is more significant for random
read and write. In this study, we present the experience we had with these different database management
systems including setup and configuration complexity
STORAGE GROWING FORECAST WITH BACULA BACKUP SOFTWARE CATALOG DATA MININGcsandit
Backup software information is a potential source for data mining: not only the unstructured
stored data from all other backed-up servers, but also backup jobs metadata, which is stored in
a formerly known catalog database. Data mining this database, in special, could be used in
order to improve backup quality, automation, reliability, predict bottlenecks, identify risks,
failure trends, and provide specific needed report information that could not be fetched from
closed format property stock property backup software database. Ignoring this data mining
project might be costly, with lots of unnecessary human intervention, uncoordinated work and
pitfalls, such as having backup service disruption, because of insufficient planning. The specific
goal of this practical paper is using Knowledge Discovery in Database Time Series, Stochastic
Models and R scripts in order to predict backup storage data growth. This project could not be
done with traditional closed format proprietary solutions, since it is generally impossible to
read their database data from third party software because of vendor lock-in deliberate
overshadow. Nevertheless, it is very feasible with Bacula: the current third most popular backup
software worldwide, and open source. This paper is focused on the backup storage demand
prediction problem, using the most popular prediction algorithms. Among them, Holt-Winters
Model had the highest success rate for the tested data sets.
PERFORMANCE EVALUATION OF SQL AND NOSQL DATABASE MANAGEMENT SYSTEMS IN A CLUSTERijdms
In this study, we evaluate the performance of SQL and NoSQL database management systems namely;
Cassandra, CouchDB, MongoDB, PostgreSQL, and RethinkDB. We use a cluster of four nodes to run the
database systems, with external load generators.The evaluation is conducted using data from Telenor
Sverige, a telecommunication company that operates in Sweden. The experiments are conducted using
three datasets of different sizes.The write throughput and latency as well as the read throughput and
latency are evaluated for four queries; namely distance query, k-nearest neighbour query, range query, and
region query. For write operations Cassandra has the highest throughput when multiple nodes are used,
whereas PostgreSQL has the lowest latency and the highest throughput for a single node. For read
operations MongoDB has the lowest latency for all queries. However, Cassandra has the highest
throughput for reads. The throughput decreasesas the dataset size increases for both write and read, for
both sequential as well as random order access. However, this decrease is more significant for random
read and write. In this study, we present the experience we had with these different database management
systems including setup and configuration complexity
STORAGE GROWING FORECAST WITH BACULA BACKUP SOFTWARE CATALOG DATA MININGcsandit
Backup software information is a potential source for data mining: not only the unstructured
stored data from all other backed-up servers, but also backup jobs metadata, which is stored in
a formerly known catalog database. Data mining this database, in special, could be used in
order to improve backup quality, automation, reliability, predict bottlenecks, identify risks,
failure trends, and provide specific needed report information that could not be fetched from
closed format property stock property backup software database. Ignoring this data mining
project might be costly, with lots of unnecessary human intervention, uncoordinated work and
pitfalls, such as having backup service disruption, because of insufficient planning. The specific
goal of this practical paper is using Knowledge Discovery in Database Time Series, Stochastic
Models and R scripts in order to predict backup storage data growth. This project could not be
done with traditional closed format proprietary solutions, since it is generally impossible to
read their database data from third party software because of vendor lock-in deliberate
overshadow. Nevertheless, it is very feasible with Bacula: the current third most popular backup
software worldwide, and open source. This paper is focused on the backup storage demand
prediction problem, using the most popular prediction algorithms. Among them, Holt-Winters
Model had the highest success rate for the tested data sets.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAPREDUCE FOR MINING ASSOCIATION...IJDKP
In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an
algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively
constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth)
algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth
based on MapReduce framework using Hadoop approach. New method has high achieving
performance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets to
discovery frequent patterns in a transaction database. Based on our method, the execution time under
different minimum supports is decreased..
Probabilistic Data Structures and Approximate SolutionsOleksandr Pryymak
Will your decisions change if you'll know that the audience of your website isn't 5M users, but rather 5'042'394'953? Unlikely, so why should we always calculate the exact solution at any cost? An approximate solution for this and many similar problems would take only a fraction of memory and runtime in comparison to calculating the exact solution.
Project for Insight Data Engineering
"Count me once, count me fast!": Investigating the performance of probabilistic methods and data structures (hyperloglog, Bloom filters) in real-time streaming data applications.
Video with narration available: https://youtu.be/ZRCLZ3aIaVU
Scott Bailey
Few things we model in our databases are as complicated as time. The major database vendors have struggled for years with implementing the base data types to represent time. And the capabilities and functionality vary wildly among databases. Fortunately PostgreSQL has one of the best implementations out there. We will look at PostgreSQL's core functionality, discuss temporal extensions, modeling temporal data, time travel and bitemporal data.
A Survey on Improve Efficiency And Scability vertical mining using Agriculter...Editor IJMTER
Basic idea is that the search tree could be divided into sub process of equivalence
classes. And since generating item sets in sub process of equivalence classes is independent from
each other, we could do frequent item set mining in sub trees of equivalence classes in parallel. So
the straightforward approach to parallelize Éclat is to consider each equivalence class as a data
(agriculture). We can distribute data to different nodes and nodes could work on data without any
synchronization. Even though the sorting helps to produce different sets in smaller sizes, there is a
cost for sorting. Our Research to analysis is that the size of equivalence class is relatively small
(always less than the size of the item base) and this size also reduces quickly as the search goes
deeper in the recursion process. Base on time using more than using agriculture data we can handle
large amount of data so first we develop éclat algorithm then develop parallel éclat algorithm then
compare with using same data with respect time .with the help of support and confidence.
An Efficient Compressed Data Structure Based Method for Frequent Item Set Miningijsrd.com
Frequent pattern mining is very important for business organizations. The major applications of frequent pattern mining include disease prediction and analysis, rain forecasting, profit maximization, etc. In this paper, we are presenting a new method for mining frequent patterns. Our method is based on a new compact data structure. This data structure will help in reducing the execution time.
Scientific Applications and Heterogeneous Architecturesinside-BigData.com
In this deck from ATPESC 2019, Michela Taufer from UT Knoxville presents: Scientific Applications and Heterogeneous Architectures.
"This talk discusses two emerging trends in computing (i.e., the convergence of data generation and analytics, and the emergence of edge computing) and how these trends can impact heterogeneous applications. Next-generation supercomputers, with their extremely heterogeneous resources and dramatically higher performance than current systems, will generate more data than we need or, even, can handle. At the same time, more and more data is generated at the “edge,” requiring computing and storage to move closer and closer to data sources. The coordination of data generation and analysis across the spectrum of heterogonous systems including supercomputers, cloud computing, and edge computing adds additional layers of heterogeneity to applications’ workflows. More importantly, the coordination can neither rely on manual, centralized approaches as it is predominately done today in HPC nor exclusively be delegated to be just a problem for commercial Clouds. This talk presents case studies of heterogenous applications in precision medicine and precision farming that expand scientist workflows beyond the supercomputing center and shed our reliance on large-scale simulations exclusively, for the sake of scientific discovery."
Watch the video: https://wp.me/p3RLHQ-lq2
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
A Survey of Sequential Rule Mining Techniquesijsrd.com
In this paper, we present an overview of existing sequential rule mining algorithms. All these algorithms are described more or less on their own. Sequential rule mining is a very popular and computationally expensive task. We also explain the fundamentals of sequential rule mining. We describe today's approaches for sequential rule mining. From the broad variety of efficient algorithms that have been developed we will compare the most important ones. We will systematize the algorithms and analyze their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behavior of the algorithms is much more similar as to be expected.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Mining Regular Patterns in Data Streams Using Vertical FormatCSCJournals
The increasing prominence of data streams has been lead to the study of online mining in order to capture interesting trends, patterns and exceptions. Recently, temporal regularity in occurrence behavior of a pattern was treated as an emerging area in several online applications like network traffic, sensor networks, e-business and stock market analysis etc. A pattern is said to be regular in a data stream, if its occurrence behavior is not more than the user given regularity threshold. Although there has been some efforts done in finding regular patterns over stream data, no such method has been developed yet by using vertical data format. Therefore, in this paper we develop a new method called VDSRP-method to generate the complete set of regular patterns over a data stream at a user given regularity threshold. Our experimental results show that highly efficiency in terms of execution and memory consumption.
Data mining is a very popular research topic over the years. Sequential pattern mining or sequential rule mining is very useful application of data mining for the prediction purpose. In this paper, we have presented a review over sequential rule cum sequential pattern mining. The advantages & drawbacks of each popular sequential mining method is discussed in brief.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAPREDUCE FOR MINING ASSOCIATION...IJDKP
In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an
algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively
constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth)
algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth
based on MapReduce framework using Hadoop approach. New method has high achieving
performance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets to
discovery frequent patterns in a transaction database. Based on our method, the execution time under
different minimum supports is decreased..
Probabilistic Data Structures and Approximate SolutionsOleksandr Pryymak
Will your decisions change if you'll know that the audience of your website isn't 5M users, but rather 5'042'394'953? Unlikely, so why should we always calculate the exact solution at any cost? An approximate solution for this and many similar problems would take only a fraction of memory and runtime in comparison to calculating the exact solution.
Project for Insight Data Engineering
"Count me once, count me fast!": Investigating the performance of probabilistic methods and data structures (hyperloglog, Bloom filters) in real-time streaming data applications.
Video with narration available: https://youtu.be/ZRCLZ3aIaVU
Scott Bailey
Few things we model in our databases are as complicated as time. The major database vendors have struggled for years with implementing the base data types to represent time. And the capabilities and functionality vary wildly among databases. Fortunately PostgreSQL has one of the best implementations out there. We will look at PostgreSQL's core functionality, discuss temporal extensions, modeling temporal data, time travel and bitemporal data.
A Survey on Improve Efficiency And Scability vertical mining using Agriculter...Editor IJMTER
Basic idea is that the search tree could be divided into sub process of equivalence
classes. And since generating item sets in sub process of equivalence classes is independent from
each other, we could do frequent item set mining in sub trees of equivalence classes in parallel. So
the straightforward approach to parallelize Éclat is to consider each equivalence class as a data
(agriculture). We can distribute data to different nodes and nodes could work on data without any
synchronization. Even though the sorting helps to produce different sets in smaller sizes, there is a
cost for sorting. Our Research to analysis is that the size of equivalence class is relatively small
(always less than the size of the item base) and this size also reduces quickly as the search goes
deeper in the recursion process. Base on time using more than using agriculture data we can handle
large amount of data so first we develop éclat algorithm then develop parallel éclat algorithm then
compare with using same data with respect time .with the help of support and confidence.
An Efficient Compressed Data Structure Based Method for Frequent Item Set Miningijsrd.com
Frequent pattern mining is very important for business organizations. The major applications of frequent pattern mining include disease prediction and analysis, rain forecasting, profit maximization, etc. In this paper, we are presenting a new method for mining frequent patterns. Our method is based on a new compact data structure. This data structure will help in reducing the execution time.
Scientific Applications and Heterogeneous Architecturesinside-BigData.com
In this deck from ATPESC 2019, Michela Taufer from UT Knoxville presents: Scientific Applications and Heterogeneous Architectures.
"This talk discusses two emerging trends in computing (i.e., the convergence of data generation and analytics, and the emergence of edge computing) and how these trends can impact heterogeneous applications. Next-generation supercomputers, with their extremely heterogeneous resources and dramatically higher performance than current systems, will generate more data than we need or, even, can handle. At the same time, more and more data is generated at the “edge,” requiring computing and storage to move closer and closer to data sources. The coordination of data generation and analysis across the spectrum of heterogonous systems including supercomputers, cloud computing, and edge computing adds additional layers of heterogeneity to applications’ workflows. More importantly, the coordination can neither rely on manual, centralized approaches as it is predominately done today in HPC nor exclusively be delegated to be just a problem for commercial Clouds. This talk presents case studies of heterogenous applications in precision medicine and precision farming that expand scientist workflows beyond the supercomputing center and shed our reliance on large-scale simulations exclusively, for the sake of scientific discovery."
Watch the video: https://wp.me/p3RLHQ-lq2
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
A Survey of Sequential Rule Mining Techniquesijsrd.com
In this paper, we present an overview of existing sequential rule mining algorithms. All these algorithms are described more or less on their own. Sequential rule mining is a very popular and computationally expensive task. We also explain the fundamentals of sequential rule mining. We describe today's approaches for sequential rule mining. From the broad variety of efficient algorithms that have been developed we will compare the most important ones. We will systematize the algorithms and analyze their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behavior of the algorithms is much more similar as to be expected.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Mining Regular Patterns in Data Streams Using Vertical FormatCSCJournals
The increasing prominence of data streams has been lead to the study of online mining in order to capture interesting trends, patterns and exceptions. Recently, temporal regularity in occurrence behavior of a pattern was treated as an emerging area in several online applications like network traffic, sensor networks, e-business and stock market analysis etc. A pattern is said to be regular in a data stream, if its occurrence behavior is not more than the user given regularity threshold. Although there has been some efforts done in finding regular patterns over stream data, no such method has been developed yet by using vertical data format. Therefore, in this paper we develop a new method called VDSRP-method to generate the complete set of regular patterns over a data stream at a user given regularity threshold. Our experimental results show that highly efficiency in terms of execution and memory consumption.
Data mining is a very popular research topic over the years. Sequential pattern mining or sequential rule mining is very useful application of data mining for the prediction purpose. In this paper, we have presented a review over sequential rule cum sequential pattern mining. The advantages & drawbacks of each popular sequential mining method is discussed in brief.
Mining Top-k Closed Sequential Patterns in Sequential Databases IOSR Journals
Abstract: In data mining community, sequential pattern mining has been studied extensively. Most studies
require the specification of minimum support threshold to mine the sequential patterns. However, it is difficult
for users to provide an appropriate threshold in practice. To overcome this, we propose mining top-k closed
sequential patterns of length no less than min_l, where k is the number of closed sequential patterns to be
mined, and min_l is the minimum length of each pattern. We mine closed patterns since they are solid
representations of frequent patterns.
Keywords: closed pattern, data mining, sequential pattern, scalability
Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. This does not automatically guarantee that it will be highly efficient since one may still encounter the combinatorial problem of candidate generation if one simply uses this FP-tree to generate and check all the candidate patterns. we study how to explore the compact information stored in an FP-tree, develop the principles of frequent-pattern growth by examination of our running example, explore how to perform further optimization when there exit a single prefix path in an FP-tree, and propose a frequent- pattern growth algorithm, FP-growth, for mining the complete set of frequent patterns using FP-tree.
In this paper, we have proposed a novel sequential mining method. The method is fast in comparison to existing method. Data mining, that is additionally cited as knowledge discovery in databases, has been recognized because the method of extracting non-trivial, implicit, antecedently unknown, and probably helpful data from knowledge in databases. The information employed in the mining method usually contains massive amounts of knowledge collected by computerized applications. As an example, bar-code readers in retail stores, digital sensors in scientific experiments, and alternative automation tools in engineering typically generate tremendous knowledge into databases in no time. Not to mention the natively computing- centric environments like internet access logs in net applications. These databases therefore work as rich and reliable sources for information generation and verification. Meanwhile, the massive databases give challenges for effective approaches for information discovery.
Graph based Approach and Clustering of Patterns (GACP) for Sequential Pattern...AshishDPatel1
The sequential pattern mining generates the sequential patterns. It can be used as the input of another program for retrieving the information from the large collection of data. It requires a large amount of memory as well as numerous I/O operations. Multistage operations reduce the efficiency of the
algorithm. The given GACP is based on graph representation and avoids recursively reconstructing intermediate trees during the mining process. The algorithm also eliminates the need of repeatedly scanning the database. A graph used in GACP is a data structure accessed starting at its first node called root and each node of a graph is either a leaf or an interior node. An interior node has one or more child nodes, thus from the root to any node in the graph defines a sequence. After construction of the graph the pruning technique called clustering is used to retrieve the records from the graph. The algorithm can be used to mine the database using compact memory based data structures and cleaver pruning methods.
K-means Clustering Method for the Analysis of Log Dataidescitation
Clustering analysis method is one of the main
analytical methods in data mining; the method of clustering
algorithm will influence the clustering results directly. This
paper discusses the standard k-means clustering algorithm
and analyzes the shortcomings of standard k-means
algorithm. This paper also focuses on web usage mining to
analyze the data for pattern recognition. With the help of k-
means algorithm, pattern is identified.
Mining closed sequential patterns in large sequence databasesijdms
Sequential pattern mining is studied widely in the data mining community. Finding sequential patterns is a basic data mining method with broad applications. Closed sequential pattern mining is an important technique among the different types of sequential pattern mining, since it preserves the details of the full pattern set and it is more compact than sequential pattern mining. In this paper, we propose an efficient algorithm CSpan for mining closed sequential patterns. CSpan uses a new pruning method called occurrence checking that allows the early detection of closed sequential patterns during the mining process. Our extensive performance study on various real and synthetic datasets shows that the proposed algorithm CSpan outperforms the CloSpan and a recently proposed algorithm ClaSP by an order of magnitude.
Opportunities for X-Ray science in future computing architecturesIan Foster
The world of computing continues to evolve rapidly. In just the past 10 years, we have seen the emergence of petascale supercomputing, cloud computing that provides on-demand computing and storage with considerable economies of scale, software-as-a-service methods that permit outsourcing of complex processes, and grid computing that enables federation of resources across institutional boundaries. These trends shown no signs of slowing down: the next 10 years will surely see exascale, new cloud offerings, and terabit networks. In this talk I review various of these developments and discuss their potential implications for a X-ray science and X-ray facilities.
Abstract: Sequential pattern mining, which discovers the correlation relationships from the ordered list of
events, is an important research field in data mining area. In our study, we have developed a Sequential
Pattern Tree structure to store both frequent and non-frequent items from sequence database. It requires only
one scan of database to build the tree due to storage of non-frequent items which reduce the tree construction
time considerably. Then, we have proposed an efficient Sequential Pattern Tree Mining algorithm which can
generate frequent sequential patterns from the Sequential Pattern Tree recursively. The main advantage of this
algorithm is to mine the complete set of frequent sequential patterns from the Sequential Pattern Tree without
generating any intermediate projected tree. Again, it does not generate unnecessary candidate sequences and
not require repeated scanning of the original database. We have compared our proposed approach with three
existing algorithms and our performance study shows that, our algorithm is much faster than apriori based GSP
algorithm and also faster than existing PrefixSpan and Tree Based Mining algorithm which are based on
pattern growth approaches.
Keywords: Data Mining, Sequence Database, Sequential Pattern, Sequential Pattern Mining, Frequent
Patterns, Tree Based Mining.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a
partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor
networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in
colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs).
This method tries to find all colocations that are to be generated from a random world. For this we first
apply an approximation error to find all the PPCs which reduce the computations. Next find all the
possible worlds and split them into two different worlds and compute the prevalence probability. These
worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic
Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant
improvement in computational time in comparison to some of the existing methods used in colocation
mining.
Drsp dimension reduction for similarity matching and pruning of time series ...IJDKP
Similarity matching and join of time series data streams has gained a lot of relevance in today’s world that
has large streaming data. This process finds wide scale application in the areas of location tracking,
sensor networks, object positioning and monitoring to name a few. However, as the size of the data stream
increases, the cost involved to retain all the data in order to aid the process of similarity matching also
increases. We develop a novel framework to addresses the following objectives. Firstly, Dimension
reduction is performed in the preprocessing stage, where large stream data is segmented and reduced into
a compact representation such that it retains all the crucial information by a technique called Multi-level
Segment Means (MSM). This reduces the space complexity associated with the storage of large time-series
data streams. Secondly, it incorporates effective Similarity Matching technique to analyze if the new data
objects are symmetric to the existing data stream. And finally, the Pruning Technique that filters out the
pseudo data object pairs and join only the relevant pairs. The computational cost for MSM is O(l*ni) and
the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction Factor. We have
performed exhaustive experimental trials to show that the proposed framework is both efficient and
competent in comparison with earlier works.
MMP-TREE FOR SEQUENTIAL PATTERN MINING WITH MULTIPLE MINIMUM SUPPORTS IN PROG...IJCSEA Journal
Although there may be lot of research work done on sequential pattern mining in static, incremental, progressive databases, the previous work do not fully concentrating on support issues. Most of the previous approaches set a single minimum support threshold for all the items or item sets. But in real world applications different items may have different support threshold to describe whether a given item or item set is a frequent item set. This means each item will contain its own support threshold depends upon various issues like cost of item, environmental factors etc. In this work we proposed a new approach which can be applied on any algorithm independent of that whether the particular algorithm may or may not use the process of generating the candidate sets for identifying the frequent item sets. The proposed algorithm will use the concept of “percentage of participation” instead of occurrence frequency for every possible combination of items or item sets. The concept of percentage of participation will be calculated based on the minimum support threshold for each item set. Our algorithm MS-PISA , which stands for Multiple Support Progressive mIning Of Sequential pAtterns , which discovers sequential patterns in by considering different multiple minimum support threshold values for every possible combinations of item or item sets.
Similar to A survey paper on sequence pattern mining with incremental (20)
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A survey paper on sequence pattern mining with incremental
1. Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol.6, No.6, 2015
25
A Survey Paper on Sequence Pattern Mining with Incremental
Approach
Rutva Patel
G.E.C,Modasa
rutva88@gmail.com
Prof. J.S. Dhobi
G.E.C,Modasa
jsdhobi@yahoo.com
Abstract:
Sequential pattern mining finds frequently occurring patterns ordered by time. The problem was first introduced
by Agrawal and Srikant [1]. An example of a sequential pattern is “A customer who purchased a new Ford
Explorer two years ago, is likely to respond favourably to a trade-in option now”. Let X be the clause “purchased
a new Ford Explorer” and Y be the clause “responds favourably to a trade-in”. Then notice that the pattern XY
above, is different from pattern YX which states that “A customer who responded favourably to a trade-in two
years ago, will purchase a Ford Explorer now”. The order in which X and Y appear is important, and hence XY
and YX are mined as two separate patterns.Sequential pattern mining is widely applicable since many types of
data have a time component to them. For example, it can be used in the medical domain to help determine a
correct diagnosis from the sequence of symptoms experienced; over customer data to help target repeat
customers; and with web-log data to better structure a company’s website for easier access to the most popular
links[2].
1. INTRODUCTION
An itemset is a non-empty set of items. A sequence is an ordered list of itemsets. Without loss of generality, we
assume that the set of items is mapped to a set of contiguous integers. We denote an itemset i by (i1, i2, i3, ... im)
where ij is an item1. We denote a sequence s by <s1, s2, s3, ... sn> where sj is an itemset.
A sequence <a1, a2, a3, ... an> is contained in another sequence <b1, b2, b3, ... bm> if there exists integers
k1 < k2 < ... < kn such that a1 ⊆ bk1, a2 ⊆ bk2, ... an ⊆ bkn. For example, the sequence <(3) (4 5) (8)> is contained
in <(7) (3 8) (9) (4 5 6) (8)>, since (3) ⊆ (3 8), (4 5) ⊆ (4 5 6) and (8) ⊆ (8). However, the sequence <(3) (5)> is
not contained in <(3 5)> and vice versa. The former represents items 3 and 5 being bought one after the other,
while the latter represent items 3 and 5 being bought together.
A record supports a sequence s if s is contained in it. The support count is incremented only once per
record. The support for a sequence is defined as the fraction of the whole data set that contains this sequence. If
this support ≤ min_sup, then the sequence is frequent.
The four algorithms are surveyed one by one.
1.1 GSP
It is an Apriori based algorithm for sequential pattern mining[3]. The difference is that GSP inserts some
constraints into the mining process, i.e., time constraints, and relaxes the definition of transaction. Moreover, it
takes the taxonomies into account. For time constraints, maximum gap and minimal gap are defined to specified
the gap between any two adjacent transactions in the sequence. If the distance between two transactions is not in
the range between the maximum gap and the minimal gap, then the two transactions can not be taken as two
consecutive transactions in a sequence.
1.2 ISM
The ISM algorithm, proposed by [4], is actually an extension of SPADE, which aims at considering the update
by means of the negative border and a rewriting of the database. The first step of ISM aims at pruning the
sequences that become infrequent from the set of frequent sequences after the update. One scan of the database is
enough to update the lattice as well as the negative border. The second step aims at taking into account the new
frequent sequences one by one, in order to make the information browse the lattice using the SPADE generating
process.
1.3 FREESPAN
FreeSpan [6] was developed to substantially reduce the expensive candidate generation and testing of Apriori,
while maintaining its basic heuristic. In general, FreeSpan uses frequent items to recursively project the sequence
database into projected databases while growing subsequence fragments in each projected database. Each
2. Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol.6, No.6, 2015
26
projection partitions the database and confines further testing to progressively smaller and more manageable
units.
A big advantage of FreeSpan over GSP is that it successfully confines pattern generation to
progressively smaller projected databases. This not only reduces the number of candidates to be checked at any
one time, but also limits candidate generation to only those items and known large-k patterns guaranteed to form
at least one large-k+1 pattern (even then the number of candidates may be substantial). However, FreeSpan also
has some non-trivial costs:
the growth of a subsequence is explored at any split point in a candidate sequence resulting in several
possible new subsequences. For example, a frequent subsequence {cd}1
and item b, could grow into any or all of
the following subsequences if present: <bcd>, <(bc)d>, <b(cd)>, <bdc>, <(bd)c>, <cbd>, <c(bd)>, <dbc>,
<d(bc)>, <cdb>, <(cd)b>, <dcb>. Even then, this is not the full range of possible subsequences if repeating items
occur.
1.4 PREFIXSPAN
PrefixSpan [2] utilizes the method of database projection to make the database for next pass much smaller and
consequently make the algorithm more speedy. The au- thors claimed that in PrefixSpan there is no need for
candidates generation. It recursively projects the database by already found short length patterns. This pattern
growth idea is similar to that in Apriori heuristic.
For example, with the frequent 1-sequence as the prefix, the projected database is shown in Table-1.
The projected databases only contain the suffix of these sequences, by scanning the projected database all the
length-2 sequential patterns that have the parent length-1 sequential patterns as prefix can be generated. Then the
projected database is partitioned again by those length-2 sequential patterns.
Table-1
prefixSpan Mining
The main cost of PrefixSpan is the projected database scanning process. In order to improve the performance a
bi-level projection method that uses the triangle S-Matrix is introduced. The main problem of PrefixSpan, is the
time consuming on scanning the projected database, which may be very large if the original dataset is huge.
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ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol.6, No.6, 2015
27
2. CONCLUSION
Mining sequential patterns from the large transactional database is a very crucial task. There are many
approaches that have been discussed; nearly all of the previous studies were using GSP approach and PrefixSpan
approach for extracting the sequential patterns, which have scope for improvement. Thus the goal of this
research was to find a scheme for pulling the sequential patterns out of the transactional data sets considering the
time consumption.
References
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[3] R. Srikant; R. Agrawal; , “Mining sequential patterns: Generalizations and performance improvements,” In
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[4] S. Parthasarathy; M. Zaki; M. Ogihara; S. Dwarkadas; , “Incremental and interactive sequence mining,” In
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[5] J. Ayres; J. Gehrke; T. Yiu; J. Flannick; , “Sequential pattern mining using a bitmap representation,” In
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[6] J. Han; J. Pei; B. Mortazavi-Asl; Q. Chen; U. Dayal; M-C. Hsu; , “FreeSpan: Frequent Pattern-Projected
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[8] Jiawei Han and Micheline Kamber, Data MiningConcepts and Techniques - Third Edition,
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[11] Lamine M. Aouad, Nhien-An Le-Khac and Tahar M. Kechadi, "Distributed Frequent Itemsets Mining in
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