Ruan, Da, Li, Tian-Rui, Xu, Yang et al. · SCK CEN Institutional Repository · 2005
Sequential pattern discovery has emerged as an important research topic in knowledge discovery and data mining with broad applications. Previous research is mainly focused on investigating scalable algorithms for mining sequential patterns while less on its theoretical foundations. However, the latter is also important because it can help to use existing theories and methods to support more effective mining tasks. In this chapter, we onduct a systematic study on models and algorithms in sequential pattern analysis, especially discuss the existing algorithms' advantages and limitations. Then, we build the relation between the closed sequential patterns and fixed point, which can serve as a theoretical foundation of sequential patterns. Finally, we discuss\nits applications and outline the future research work.
Cited by 0
Tisoň, Zdeněk · Brno University of Technology Digital Library (Brno University of Technology) · 2012
Tato diplomová práce je zaměřena na problematiku získávání znalostí z databází, především pak na metody dolování sekvenčních vzorů. Jednotlivé metody dolování sekvenčních vzorů jsou zde popsány detailně. Dále se práce zabývá rozšířením analytických služeb platformy Microsoft SQL Server o nové dolovací algoritmy. V praktické části této práce jsou implementovány rozšíření pro dolování sekvenčních vzorů na platformě MS SQL Server. V poslední části jsou vytvořené algoritmy porovnány nad různými datovými sadami.
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Tianrui Li, Xu Yang, Da Ruan et al. · Studies in computational intelligence · 2005
Cited by 14
Julien Rabatel, Sandra Bringay, Pascal Poncelet · HAL (Le Centre pour la Communication Scientifique Directe) · 2010
Traditional sequential patterns do not take into account additional contextual information since patterns extracted from data are usually general. By considering the fact that a pattern is associated with one specific context the decision expert can then adapt his strategy considering the type of customers. In this paper we propose to mine more precise patterns of the form "young users buy products A and B then product C, while old users do not follow this same behavior". By highlighting relevant properties of such contexts, we show how contextual sequential patterns can be extracted by mining the database in a concise manner. We conduct our experimental evaluation on real-world data and demonstrate performance issues.
Cited by 10Open access
Ashin Ara Bithi · IOSR Journal of Computer Engineering · 2013
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.
Cited by 3Open access
Iosr Journals, Abu Ahmed Ferdaus Ashin Ara Bithi · Figshare · 2014
Cited by 0Open access
Zhedong Zheng · UTS ePRESS (University of Technology Sydney) · 2012
Following that, NSP mining case studies coming from health insurance industry are introduced.Based on real-life customer claims datasets, we use the proposed NSP mining methods to find PSP and NSP on solving two business issues, one is in ancillary service over-service analysis, another is fraud claim detection.Both of the two case studies demonstrate the benefits gained from mining NSP.xiv
Cited by 6Open access
Qiankun Zhao, Sourav S. Bhowmick · 2003
Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Also data mining is known as one of the core processes of Knowledge Discovery in Database (KDD). Many people take data mining as a synonym for another popular term, Knowledge Discovery in Database (KDD). Alternatively other people treat Data Mining as the core process of KDD. The KDD processes are shown in Figure 1 [Han and Kamber 2000]. Usually there are three processes. One is called preprocessing, which is executed before data mining techniques are applied to the right data. The preprocessing includes data cleaning, integration, selection and transformation. The main process of KDD is the data mining process, in this process different algorithms are applied to produce hidden knowledge. After that comes another process called postprocessing, which evaluates the mining result according to users’ requirements and domain knowledge. Regarding the evaluation results, the knowledge can be presented if the result is satisfactory, otherwise we have to run some or all of those processes again until we get the satisfactory result. The actually processes work as follows. First we need to clean and integrate the databases. Since the data source may come from different databases, which may have some inconsistences and duplications, we must clean the data source by removing those noises or make some compromises. Suppose we have two different databases, different words are used to refer the same thing in their schema. When we try to integrate the two sources we can only choose one of them, if we know that they denote the same thing. And also real world data tend to be incomplete and noisy due to the manual input mistakes. The integrated data sources can be stored in a database, data warehouse or other repositories. As not all the data in the database are related to our mining task, the second process is to select task related data from the integrated resources and transform them into a format that is ready to be mined. Suppose we want to find which items are often purchased together in a supermarket, while the database that records the purchase history may contains customer ID, items bought, transaction time, prices, number of each items and so on, but for this specific task we only need items bought. After selection of relevant data, the database that we are going to apply our data mining techniques to will be much smaller, consequently the whole process will be
Cited by 134
Nikhil Gundawar, Venkatesh Akolekar, Piyush Phalak et al. · 2015
As the instability in environment is increasing day by day, the task of forecasting has become tedious and gradually difficult. Prediction of natural disasters using technology requires extensive research and funding. In current scenario usually constant surveillance is used for this task. By monitoring ocean currents, weather patterns can be predicted in advance, warning populated areas under risk of hurricanes and tornados. However, these short-term warnings are effective only if relief programs are planned and efficiently carried out. We are focused on making these warnings earlier with the help of sequential pattern data mining in which trends found in similar events from past are used to predict climatic hazards like cloudburst and hailstorms.
Cited by 0
Helen Pinto, Jiawei Han, Jian Pei et al. · 2001
Sequential pattern mining, which finds the set of frequent subsequences in sequence databases, is an important data-mining task and has broad applications. Usually, sequence patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine sequential patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional sequential pattern mining, which integrates the multidimensional analysis and sequential data mining. We also thoroughly explore efficient methods for multi-dimensional sequential pattern mining. We examine feasible combinations of efficient sequential pattern mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.
Cited by 198
Helen Pinto, Jiawei Han, Jian Pei et al. · 2001
Sequential pattern mining, which finds the set of frequent subsequences in sequence databases, is an important data-mining task and has broad applications. Usually, sequence patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine sequential patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional sequential pattern mining, which integrates the multidimensional analysis and sequential data mining. We also thoroughly explore efficient methods for multi-dimensional sequential pattern mining. We examine feasible combinations of efficient sequential pattern mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.
Cited by 28
Michał R. Przybyłek · International Journal of Computer Theory and Engineering · 2014
The basic idea behind the skeletal algorithm is to express a problem in terms of congruences on a structure, build an initial set of congruences, and improve it by taking limited unions/intersections, until a suitable condition is reached. Skeletal algorithms naturally arise in the context of data/process mining, where the skeleton is the "free" structure on initial data and congruence corresponds to similarities in data. In this paper we study skeletal algorithms applied to sequential pattern mining and compare their performance with real models, Markov chains and models based on Shannon entropy.
Cited by 1Open access
Zhenqiang Ye, Ziyang Li, Weibin Guo et al. · Lecture notes in computer science · 2022
Cited by 2
Kirti S. Patil, Sandip Patil · Asian Journal of Computer Science and Technology · 2013
The concept of Sequential Pattern Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995. Sequential Patterns are used to discover sequential sub-sequences among large amount of sequential data. In web usage mining, sequential patterns are exploited to find sequential navigation patterns that appear in users’ sessions sequentially. The information obtained from sequential pattern mining can be used in marketing, medical records, sales analysis, and so on. In this paper, a new algorithm is proposed; it combines the Apriori algorithm and FP-tree structure which proposed in FP-growth algorithm. The advantage of proposed algorithm is that it dosen’t need to generate conditional pattern bases and sub-conditional pattern tree recursively. And the results of the experiments show that it works faster than Apriori.
Cited by 0Open access
Huynh, Minh Huy, Pham, Ngoc Nam, Komínková Oplatková, Zuzana et al. · Repository of TBU publications (Univerzita Tomase Bati ze Zline) · 2020
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Iosr Journals, Z.M. Tarek Shahriar Ashin Ara Bithi · Figshare · 2014
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Gengsen Huang, Wensheng Gan, Philip S. Yu · ACM Transactions on Knowledge Discovery from Data · 2024
Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this article, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.
Cited by 25
Shaotao Chen, Jiahui Chen, Shicheng Wan · Expert Systems with Applications · 2023
Cited by 9
Tingfu Zhou, Wensheng Gan, Zhenlian Qi · 2022
In real-life, big data is contiguous, such as traffic flow and network flow, and thus some contiguous mining algorithms have been developed. It has been noticed that the significance of data (e.g., DNA sequences) is often different, and the real data may have various weights. However, the continuity of the mined data is not fully considered in the existing weighted mining algorithms. In this study, we are the first to formulate the problem of mining weighted contiguous sequential patterns and propose a new algorithm named WCSpan. Based on the usage of modified prefix pattern expansion and a tight weighted upper-bound model, we proved that WCSpan can efficiently mine the weighted contiguous sequential patterns. Experimental results showed that compared with existing similar algorithms, the proposed algorithm has advantages in execution time and memory usage. Besides, the integrity of the outcome patterns of WCSpan is preserved while data omission is avoided. In addition, the generation of patterns by the WCSpan method is faster than other methods, where the weighted upper-bound model can prune redundant candidates precisely to save memory. Both of them significantly improve the performance of WCSpan.
Cited by 0
周清江 · 2008
計畫編號：NSC97-2221-E032-046
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