This document summarizes a research paper on mining relevant time-interval weighted sequential (TiWS) patterns from sequence databases with reduced search space. It proposes using the CloSpan algorithm to mine TiWS patterns, which considers the time intervals between elements in sequences to assign weights and find more interesting patterns. Calculating weights based on time intervals allows filtering out patterns with large time intervals, reducing the number of database scans needed. The CloSpan algorithm further prunes the search space to improve efficiency over other sequential pattern mining methods like PrefixSpan. Experimental results show the combined TiWS and CloSpan approach provides more relevant patterns with reduced runtime compared to alternatives.
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.
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.
Novel Ensemble Tree for Fast Prediction on Data StreamsIJERA Editor
Data Streams are sequential set of data records. When data appears at highest speed and constantly, so predicting
the class accordingly to the time is very essential. Currently Ensemble modeling techniques are growing
speedily in Classification of Data Stream. Ensemble learning will be accepted since its benefit to manage huge
amount of data stream, means it will manage the data in a large size and also it will be able to manage concept
drifting. Prior learning, mostly focused on accuracy of ensemble model, prediction efficiency has not considered
much since existing ensemble model predicts in linear time, which is enough for small applications and
accessible models workings on integrating some of the classifier. Although real time application has huge
amount of data stream so we required base classifier to recognize dissimilar model and make a high grade
ensemble model. To fix these challenges we developed Ensemble tree which is height balanced tree indexing
structure of base classifier for quick prediction on data streams by ensemble modeling techniques. Ensemble
Tree manages ensembles as geodatabases and it utilizes R tree similar to structure to achieve sub linear time
complexity
An Evaluation and Overview of Indices Based on Arabic DocumentsIJCSEA Journal
The paper aims at giving an overview about inverted files , signature files, suffix array and suffix tree based on Arabic documents collection. The paper also aims at giving the comparison points between all these techniques and the performance of this techniques on each of the comparison points. Any information retrieval System is usually evaluated through efficiency and effectiveness of this system. Moreover, there are two aspects of efficiency: Time and Space. The time measure represents the time needed to retrieve a document relevant to a specified query, while space represents the capacity of memory needed to create the two indices.
An Evaluation and Overview of Indices Based on Arabic DocumentsIJCSEA Journal
The paper aims at giving an overview about inverted files , signature files, suffix array and suffix tree based on Arabic documents collection. The paper also aims at giving the comparison points between all these techniques and the performance of this techniques on each of the comparison points. Any information retrieval System is usually evaluated through efficiency and effectiveness of this system. Moreover, there are two aspects of efficiency: Time and Space. The time measure represents the time needed to retrieve a document relevant to a specified query, while space represents the capacity of memory needed to create the two indices.
In this paper, four indices will be built: inverted-file , signature-file, suffix array and suffix tree. However, to measure the performance of each one, a retrieval system must be built to compare the results of using these indices.
A collection of 242 Arabic Abstracts from the proceeding of the Saudi Arabian National Computer Conferences have been used in these systems, and a collection of 60 Arabic queries have been run on the there systems. We found out that the retrieval result for inverted files is better than the retrieval result for other indices.
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.
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.
Novel Ensemble Tree for Fast Prediction on Data StreamsIJERA Editor
Data Streams are sequential set of data records. When data appears at highest speed and constantly, so predicting
the class accordingly to the time is very essential. Currently Ensemble modeling techniques are growing
speedily in Classification of Data Stream. Ensemble learning will be accepted since its benefit to manage huge
amount of data stream, means it will manage the data in a large size and also it will be able to manage concept
drifting. Prior learning, mostly focused on accuracy of ensemble model, prediction efficiency has not considered
much since existing ensemble model predicts in linear time, which is enough for small applications and
accessible models workings on integrating some of the classifier. Although real time application has huge
amount of data stream so we required base classifier to recognize dissimilar model and make a high grade
ensemble model. To fix these challenges we developed Ensemble tree which is height balanced tree indexing
structure of base classifier for quick prediction on data streams by ensemble modeling techniques. Ensemble
Tree manages ensembles as geodatabases and it utilizes R tree similar to structure to achieve sub linear time
complexity
An Evaluation and Overview of Indices Based on Arabic DocumentsIJCSEA Journal
The paper aims at giving an overview about inverted files , signature files, suffix array and suffix tree based on Arabic documents collection. The paper also aims at giving the comparison points between all these techniques and the performance of this techniques on each of the comparison points. Any information retrieval System is usually evaluated through efficiency and effectiveness of this system. Moreover, there are two aspects of efficiency: Time and Space. The time measure represents the time needed to retrieve a document relevant to a specified query, while space represents the capacity of memory needed to create the two indices.
An Evaluation and Overview of Indices Based on Arabic DocumentsIJCSEA Journal
The paper aims at giving an overview about inverted files , signature files, suffix array and suffix tree based on Arabic documents collection. The paper also aims at giving the comparison points between all these techniques and the performance of this techniques on each of the comparison points. Any information retrieval System is usually evaluated through efficiency and effectiveness of this system. Moreover, there are two aspects of efficiency: Time and Space. The time measure represents the time needed to retrieve a document relevant to a specified query, while space represents the capacity of memory needed to create the two indices.
In this paper, four indices will be built: inverted-file , signature-file, suffix array and suffix tree. However, to measure the performance of each one, a retrieval system must be built to compare the results of using these indices.
A collection of 242 Arabic Abstracts from the proceeding of the Saudi Arabian National Computer Conferences have been used in these systems, and a collection of 60 Arabic queries have been run on the there systems. We found out that the retrieval result for inverted files is better than the retrieval result for other indices.
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.
The paper aims at giving an overview about inverted files , signature files, suffix array and suffix tree
based on Arabic documents collection. The paper also aims at giving the comparison points between all
these techniques and the performance of this techniques on each of the comparison points. Any information
retrieval System is usually evaluated through efficiency and effectiveness of this system. Moreover, there
are two aspects of efficiency: Time and Space. The time measure represents the time needed to retrieve a
document relevant to a specified query, while space represents the capacity of memory needed to create
the two indices.
In this paper, four indices will be built: inverted-file , signature-file, suffix array and suffix tree. However,
to measure the performance of each one, a retrieval system must be built to compare the results of using
these indices.
A collection of 242 Arabic Abstracts from the proceeding of the Saudi Arabian National Computer
Conferences have been used in these systems, and a collection of 60 Arabic queries have been run on the
there systems. We found out that the retrieval result for inverted files is better than the retrieval result for
other indices.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESkevig
Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, structured information for public use. But for Bangla language, there is no available Freebase. So we made Bangla Freebase which was the real challenge and it can be used for any other NLP based works. Then we tried to find out the noisy patterns for relation extraction by measuring conflict score.
FP-Tree is also a huge hierarchical data structure and cannot fit into the main memory also it is not suitable for “Incremental-mining” nor used in “Interactive-mining” system
journal publishing, how to publish research paper, Call For research paper, i...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
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.
The paper aims at giving an overview about inverted files , signature files, suffix array and suffix tree
based on Arabic documents collection. The paper also aims at giving the comparison points between all
these techniques and the performance of this techniques on each of the comparison points. Any information
retrieval System is usually evaluated through efficiency and effectiveness of this system. Moreover, there
are two aspects of efficiency: Time and Space. The time measure represents the time needed to retrieve a
document relevant to a specified query, while space represents the capacity of memory needed to create
the two indices.
In this paper, four indices will be built: inverted-file , signature-file, suffix array and suffix tree. However,
to measure the performance of each one, a retrieval system must be built to compare the results of using
these indices.
A collection of 242 Arabic Abstracts from the proceeding of the Saudi Arabian National Computer
Conferences have been used in these systems, and a collection of 60 Arabic queries have been run on the
there systems. We found out that the retrieval result for inverted files is better than the retrieval result for
other indices.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESkevig
Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, structured information for public use. But for Bangla language, there is no available Freebase. So we made Bangla Freebase which was the real challenge and it can be used for any other NLP based works. Then we tried to find out the noisy patterns for relation extraction by measuring conflict score.
FP-Tree is also a huge hierarchical data structure and cannot fit into the main memory also it is not suitable for “Incremental-mining” nor used in “Interactive-mining” system
journal publishing, how to publish research paper, Call For research paper, i...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
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is an open access journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is an open access international journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Novel approach for Graphical User Interface development and real time Objec...IOSR Journals
Abstract:Image Processing is an advancement in the Signal processing domain where the input is an image or
video and output can be image, video or in any other forms. With daily advances in Human-Computer
interaction, Image Processing, along with computer vision has become an integral part of any intelligent system.
They are mainly based on the workings of the visual perception function of the human brain. As such most of
the algorithms are designed based around this central concept. The major application of Computer Vision is in
the field of intelligent machines and robotic visions, where machines are made intelligent by being able to sense
their surrounding visually and optimize themselves accordingly. In case of robots, through computer vision, they are made capable of sensing their surroundings, just like a human.
Index Terms: Image processing,GUI, Object Tracking, Face Tracking, Edge Detection
A Study on Fire Detection System using Statistic Color ModelIOSR Journals
Abstract: Normally fire detection system uses the heuristic fixed threshold values in their specific methods. However, input images may be changed, in general, so the heuristic fixed threshold values used in the fire detection systems might be modified on a case by case basis. In this paper, an automatic fire detection system without the heuristic fixed threshold values was studied. We presented an automatic method using the statistical color model and the binary background mask. We did the experiment using 600 frames from 6 typical different fire video clips. As the experimental results the proposed method showed a good performance of about average 85% detection rate without false positive, compared with the other methods with the heuristic fixed threshold values. Keywords: Emperical value,Fire detection,RGB,threshold,sensors
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.
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.
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.
An efficient algorithm for sequence generation in data miningijcisjournal
Data mining is the method or the activity of analyzing data from different perspectives and summarizing it
into useful information. There are several major data mining techniques that have been developed and are
used in the data mining projects which include association, classification, clustering, sequential patterns,
prediction and decision tree. Among different tasks in data mining, sequential pattern mining is one of the
most important tasks. Sequential pattern mining involves the mining of the subsequences that appear
frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of
customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access
patterns, seismologic data and weather observations. Various models and algorithms have been developed
for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the
efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset
by applying various performance factors. From the experimental results, it is observed that the PrefixSpan
algorithm is more efficient than other two algorithms.
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 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.
Progressive Mining of Sequential Patterns Based on Single ConstraintTELKOMNIKA JOURNAL
Data that were appeared in the order of time and stored in a sequence database can be processed to obtain sequential patterns. Sequential pattern mining is the process to obtain sequential patterns from database. However, large amount of data with a variety of data type and rapid data growth raise the scalability issue in data mining process. On the other hand, user needs to analyze data based on specific organizational needs. Therefore, constraint is used to impose limitation in the mining process. Constraint in sequential pattern mining can reduce the short and trivial sequential patterns so that the sequential patterns satisfy user needs. Progressive mining of sequential patterns, PISA, based on single constraint utilizes Period of Interest (POI) as predefined time frame set by user in progressive sequential tree. Single constraint checking in PISA utilizes the concept of anti monotonic or monotonic constraint. Therefore, the number of sequential patterns will decrease, the total execution time of mining process will decrease and as a result, the system scalability will be achieved.
Mining sequential patterns for interval basedijcsa
Sequential pattern mining finds the frequent subsequences or patterns from the given sequences.
TPrefixSpan algorithm finds the relevant frequent patterns from the given sequential patterns formed using
interval based events. In our proposed work, we add multiple constraints like item, length and aggregate to
the interval based TPrefixSpan algorithm. By adding these constraints the efficiency and effectiveness of
the algorithm improves. The proposed constraint based algorithm CTPrefixSpan has been applied to
synthetic medical dataset. The algorithm can be applied for stock market analysis, DNA sequences analysis
etc.
KEYWORDS
Sequential patterns, temporal patterns, Constraints, Interval based events.
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.
A novel algorithm for mining closed sequential patternsIJDKP
Sequential pattern mining algorithms produce an exponential number of sequential patterns when mining
long patterns or at low support thresholds. Most of the existing algorithms mine the full set of sequential patterns. However, it is sufficient to mine closed sequential patterns from which the total set of sequential patterns can be derived and the closed sequential patterns set is more compact than the sequential
patterns set. In this paper, we propose a novel algorithm NCSP for mining closed sequential patterns in large sequences databases. To the best of our knowledge, our algorithm is the first algorithm that utilizes vertical bitmap representation for closed sequential pattern mining. The results show that the proposed algorithm NCSP can find closed sequential patterns efficiently and outperforms CloSpan by an order of magnitude.
In this paper, we present a literature survey of existing frequent item set mining algorithms. The concept of frequent item set mining is also discussed in brief. The working procedure of some modern frequent item set mining techniques is given. Also the merits and demerits of each method are described. It is found that the frequent item set mining is still a burning research topic.
A Quantified Approach for large Dataset Compression in Association MiningIOSR Journals
Abstract: With the rapid development of computer and information technology in the last several decades, an
enormous amount of data in science and engineering will continuously be generated in massive scale; data
compression is needed to reduce the cost and storage space. Compression and discovering association rules by
identifying relationships among sets of items in a transaction database is an important problem in Data Mining.
Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore
it has attracted significant research attention. However, existing compression algorithms are not appropriate in
data mining for large data sets. In this research a new approach is describe in which the original dataset is
sorted in lexicographical order and desired number of groups are formed to generate the quantification tables.
These quantification tables are used to generate the compressed dataset, which is more efficient algorithm for
mining complete frequent itemsets from compressed dataset. The experimental results show that the proposed
algorithm performs better when comparing it with the mining merge algorithm with different supports and
execution time.
Keywords: Apriori Algorithm, mining merge Algorithm, quantification table
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 9, Issue 6 (Mar. - Apr. 2013), PP 47-52
www.iosrjournals.org
Relevant Tiws Pattern Mining With Reduced Search Space
J. Mercy Geraldine1, G. Shanthi Krishna2
1
HOD, Department of CSE, Srinivasan Engineering college, Perambalur, India.
2
M.E Student, Department of CSE, Srinivasan Engineering college, Perambalur, India.
Abstract: The sequential pattern mining finds all sequential patterns which occurs frequently for a given
sequence database whose frequency is no less than the threshold. The weighted sequential pattern mining aims
to find more interesting sequential patterns by considering the different significance of each data element in a
sequence database. In the conventional weighted sequential pattern mining, pre-assigned weights of data
elements are used to get the priorities which are derived from their quantitative information. Generally in
sequential pattern mining, the generation order of data is considered to find sequential patterns. However,
generation time and time-intervals between the data are also important in real world application domains.
Therefore, time-intervals information of data elements can be helpful in finding more interesting sequential
patterns. The proposed system presents a framework for finding time-interval weighted sequential (TiWS)
patterns in a sequence database. In addition, the CloSpan algorithm is used for mining TiWS patterns in a
sequence database which reduces the multiple database scans and the processing time. This is useful in
application fields such as web access pattern analysis, customer purchase pattern analysis, DNA sequence
analysis, etc.
Keywords – CloSpan, Sequential database, Sequential pattern mining, Time-interval weight, TiWS support,
TiWS pattern, weighted sequential pattern.
I. INTRODUCTION
Data mining is the process of analyzing data from different perspectives and summarizing it into useful
information. This useful information can be used to increase revenue, cuts costs, or both. Data mining is
sometimes called as data or knowledge discovery. Companies have used powerful computers to sift through
volumes of supermarket data and analyze market research reports for years. However, continuous innovations in
computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of
analysis while driving down the cost. Data mining software is one of the analytical tools for analyzing data. It
allows users to analyze data from many different angles, categorize it, and summarize the relationships
identified. Technically, data mining is the process of finding correlations or patterns among some fields in large
relational databases.
Sequential pattern mining [1] is a topic of data mining concerned with evaluating statistically relevant
patterns between data where the values are delivered in a sequence. It is usually presumed that the values are
discrete. And thus time series mining is closely related, but usually considered a different activity. Sequence
mining is a special case of structured data mining. A sequence database is a large collection of computerized
customer shopping sequence, nucleic acid sequences, protein sequences, or other sequences stored on a
computer. A database can include sequences from only one branch, or it can include sequences from multiple
branches.
In databases, a huge number of possible sequential patterns are hidden. For satisfying the minimum
support threshold, a mining algorithm should find the complete set of patterns. The mining algorithm should be
able to incorporate various kinds of user-specific constraints, highly scalable, efficient, involving only a small
few database scans. In many of the previous researches on sequential pattern mining, sequential patterns and
items in a sequential pattern had been considered uniformly. However, they have a different importance in real
world applications are considered in the sequential pattern mining. Based on this observation, prioritized time-
interval based sequential pattern mining has been proposed.
General sequential pattern mining is based on simple support counting; if weight of the information is
used, it finds interesting sequential patterns. For a sequence or a sequential pattern, not only the generation order
of data elements but also their time-intervals and generation times are important to get more valuable sequential
patterns. In a sequence database, if the importance of sequences is differentiated based on the time-intervals in
the sequences, more interesting sequential patterns can be found.
II. Related Work
Many studies have contributed to efficient mining of sequential patterns, such as GSP, MFS, SPADE,
SPAM, FreeSpan, and PrefixSpan algorithms. GSP [1] algorithm makes multiple database passes. In the first
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2. Relevant TiWS pattern mining with reduced search space
pass, all single items are counted. From the frequent items, a set of candidate 2-sequences are formed, and
another pass is made to identify their frequency, and this process is repeated until no more frequent sequences
are found. MFS [2] algorithm first mines the database samples to obtain the rough estimation of the frequent
sequences and then refines the solution.
SPADE [3] decomposes the original problem into smaller sub-problems. All frequent sequences can be
enumerated via simple temporal joins and intersections on id-lists. ID-list has a sequence id and the event id.
SPAM [4] algorithm uses a depth-first search strategy to generate candidate sequences. The transactional data is
stored using a vertical bitmap representation, which allows for efficient support counting as well as significant
bitmap compression. FreeSpan [5] is a divide and conquer approach. Frequent items are found from databases.
List of frequent items in support descending order is called f_list. FreeSpan has to keep the whole sequence in
the original database without length reduction of a sequence. Moreover, the growth of a subsequence is explored
at any split point in a candidate sequence. It is costly.
PrefixSpan[6] partitions the problem recursively. That is, each subset of sequential patterns can be
further divided into next level when necessary. PrefixSpan constructs the corresponding projected databases to
mine the subsets of sequential patterns. In these above researches on sequential pattern mining, sequential
patterns and items in the sequential patterns are considered uniformly. To improve the usefulness of mining
results in real world applications, weighted pattern mining has been studied.
Most of the weighted pattern mining [7][8] algorithms usually require pre-assigned weights, and the
weights are generally derived from the quantitative information and the importance of items in a real world
application. It does not consider the generation time of each sequence and the items within it.
ConSGapMiner[9][10][11] algorithm which uses time-interval and gap information as a constraint. However,
the algorithms just consider time-interval information between two successive items as an item, so that they
cannot support to get weighted sequential patterns based on different weights of sequences.
III. Problem Definition
Generally in sequential pattern mining, only data element order is considered. For mining sequential
patterns with time-interval, the sequences of a sequence database have its corresponding time stamp values. Let
I = {i1, i2,….,in} be a set of all items. A sequence s = <s1,s2,….sl> is an ordered list of itemsets, where sj is one of
an itemset, and its time stamp list TS(s) = <t1,t2,….,tl> which stands for the occurrence time. The number of
items in a sequence is called as the length of the sequence. For a sequence database, a tuple (sid,S) is said to
contain a sequence “a”, if ” a” is a subsequence of s. The count of a sequence A in a sequence database is the
number of tuples in the database containing a. And the support of A is the ratio of the count over the size of the
sequence database.
The proposed system considers the weight of a sequence, and uses it to find the count of a sequential
pattern and the size of a sequence database. For a given sequence database and a support threshold, the problem
of time-interval weighted sequential pattern mining is to find the complete set of all time-interval weighted
sequential patterns whose weighted supports are no less than the threshold.
IV. Weight Of A Sequence Based On Time-Interval
In mining sequential patterns, a sequence with small time-intervals among its elements can be
considered more important than that with large time-intervals. Based on this observation, an interesting mining
result of time-interval weighted sequential patterns is found for a sequence database by considering the weight
of each sequence obtained from the time-intervals in the sequence. This section presents a technique to get the
time-interval weight of a sequence for a sequence database as well as a framework for finding time-interval
weighted sequential patterns.
4.1. A time-interval between a pair of itemsets
A sequence in the sequence database consists of itemsets and their corresponding time stamps. For a
sequence S = <s1, s2, ..., sl> and its time stamp list TS(S) = <t1, t2, ..., tl>, the time-interval between two itemsets
si and sj in the sequence, that is TIij, is defined in the equation 1.
TIij = tj – ti (1)
If a sequence consists of n itemsets there exist pairs of itemsets in the sequence. Consider the
sequence S = <a,(abc),d> and its time stamp list TS(S) = <2, 3, 4>. Time interval for each possible pair in
sequence is calculated.
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3. Relevant TiWS pattern mining with reduced search space
Table 1: Possible pairs of itemsets
1st itemset 2nd itemset Time-interval
a abc 1
a d 2
abc d 1
4.2. A time-interval weight of a pair of itemsets
Normalization is needed to fairly enumerate the time-intervals of different pairs of itemsets in a sequence
database. For this purpose, the time-interval weight of the pair is found for each pair of itemsets. Let u (u > 0) be
the size of unit time and d (0 < d < 1) be a base number to determine the amount of weight reduction per unit
time u, for a sequence S = <s1, s2, ..., sl> and its time stamp list TS(S) = <t1, t2, ..., tl>, the weight of a time-
interval TIij between two itemsets si and sj are defined in equation 2.
W(TIij ) = δ power ( ) (2)
4.3. A time-interval weight of a sequence
The time-interval weight of a sequence is computed from the strength and weight of a pair of itemsets as
represented in equation 3. Among the itemsets in a sequence, a large-sized itemset may contribute more to the
sequence than a small-sized one. For this purpose strength of pair of itemsets are calculated.
(3)
Where (4)
The strength of a pair of two itemsets si and sj in the sequence STij is defined by the equation 4.
STij = length(si) × length(sj) (5)
Where length(si) denotes the number of items in si .
4.4. Time-interval weighted support
Usually, sequential pattern evaluation by support is based on simple counting in the classical sequential
pattern mining. But in this section the term TiW-support is calculated by equation 6 which is used to an
evaluation process of time-interval weighted sequential patterns in a sequence database.
(6)
For a given support threshold minSupport (0 < minSupport ≤1), a sequence X is a time-interval weighted
sequential pattern if TiW-Supp(X) is no less than the threshold, that is, TiW-Supp(X) ≥ minSupport.
V. MINING TIWS PATTERNS USING CLOSPAN ALGORITHM
In a mining process time-interval weight of each sequence in a sequence database is first obtained from
the time-intervals of elements in the sequence. Subsequently, the TiW-support of each sequential pattern is
found based on the weight, and a set of TiWS patterns is found considering the TiW-support. Then the clospan
algorithm is applied to mine the tiws patterns. Clospan has two major steps. It first generates the lattice sequence
set which is a super set of closed frequent sequences and then store it in a prefix sequence lattice. Next step is
the post-pruning to eliminate non-closed sequences.
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4. Relevant TiWS pattern mining with reduced search space
SDB minsupport
Calculate weight of
each sequence
TiW-
Supp>=m
inSupport
No
Single CloSpan
item Algorithm
Yes
Complete set of TiWS
patterns
Figure 1: A method of mining TiWS patterns
It first shorts every item and removes infrequent items. Then it calls the clospan recursively by doing
depth first search on the prefix search tree and builds the corresponding prefix sequence lattice. The second step
is the post-pruning to eliminate non-closed sequences. Clospan outperforms than prefixspan by using the search
space pruning techniques. Clospan first checks weather a discovered sequence is exists. The remaining task is to
eliminate the non-closed sequences from the prefix sequence lattice. Clospan finds all sequences that have the
same support of sequence s. Then it checks whether there is a super sequence containing s.
The sequence database, min-support and the weight function are given as inputs to this process. For
each sequence in database, weight and weighted support are calculated from the time-intervals. Then the TiWS-
support is compared with the given minsupport. If it is greater than minsupport then it is taken as time-interval
weighted pattern. The Clospan algorithm is applied on those patterns. The time-interval weight of each sequence
in a sequence database is obtained in the first scan of the sequence database, and it is used to find TiWS
patterns. In TiWS pattern mining for a sequence database, sequential patterns with large time-intervals may be
excluded from its mining result, so that the number of scans for the sequence database or its subset can be
reduced. As a result, the processing time to get the mining result can also be reduced.
VI. Performance And Evaluation
The weighted time-interval information is combined with the CloSpan algorithm to produces more
interesting sequential patterns by reducing the size of the database. This reduces the number of scans. The
performance of CloSpan algorithm related to PrefixSpan algorithm is as follows. CloSpan prunes the search
space more deeply compared to the PrefixSpan algorithm by using the pruning technique.
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5. Relevant TiWS pattern mining with reduced search space
Runtime in seconds
Support threshold
Figure 2: Comparison among CloSpan and PrefixSpan
A frequent closed sequence mining algorithm like CloSpan can definitely outperform frequent sequence
mining algorithm like PrefixSpan when the support threshold is low. Considering the time-interval information
provides most important sequences. The combination of TiWS and CloSpan algorithm provides most interesting
sequences which is closer to the user’s expectation.
Runtime in seconds
Number of sequences
Figure 3: Performance comparison.
VII. Conclusion And Future Work
The survey of the traditional sequence pattern mining algorithms has revealed that those algorithms do
not consider the weight of the time-intervals. However, considering the weight of a time-interval between the
items in each sequence is important. Therefore, an efficient algorithm called TiWS making use of CloSpan
algorithm to mine TiWS patterns is proposed. This algorithm mines the sequential patterns based on the weight
of the time-intervals, requiring the patterns to abide by the threshold value specified by the user. This provides
more interesting sequential patterns. It reduces the number of scans and the processing time. An interesting
direction for future research could be in defining other approaches to get the time-interval of a sequence and
other weighting functions.
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