Pub Date : 2020-02-27DOI: 10.15625/1813-9663/36/1/14398
Trần Huy Dương, N. Thang, V. D. Thi
High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm’s performance.
{"title":"HIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM","authors":"Trần Huy Dương, N. Thang, V. D. Thi","doi":"10.15625/1813-9663/36/1/14398","DOIUrl":"https://doi.org/10.15625/1813-9663/36/1/14398","url":null,"abstract":"High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm’s performance.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"1 1","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76250814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-27DOI: 10.15625/1813-9663/36/1/13188
Thanh Do Van, Thi Thanh Luu Le
Aggregation of knowledge bases in the propositional language was soon investigated and the requirements of aggregation processes of propositional knowledge bases basically are unified within the community of researchers and applicants. Aggregation of standard possibilistic knowledge bases where the weight of propositional formulas being numeric has also been investigated and applied in building the intelligent systems, in multi-criterion decision-making processes as well as in decisionmaking processes implemented by many people. Symbolic possibilistic logic (SPL for short) where the weight of the propositional formulas is symbols was proposed, and recently it was proven that SPL is soundness and completeness. In order to apply SPL in building intelligent systems as well as in decision-making processes, it is necessary to solve the problem of aggregation of symbolic possibilistic knowledge bases (SPK bases for short). This problem has not been researched so far. The purpose of this paper is to investigate aggregation processes of SPK bases from the postulate point of view in propositional language. These processes are implemented via impossibility distributions defined from SPK bases. Characteristics of merging operators, including hierarchical merging operators, of symbolic impossibility distributions (SIDs for short) from the postulate point of view will be shown in the paper.
{"title":"AGGREGATION OF SYMBOLIC POSSIBILISTIC KNOWLEDGE BASES FROM THE POSTULATE POINT OF VIEW","authors":"Thanh Do Van, Thi Thanh Luu Le","doi":"10.15625/1813-9663/36/1/13188","DOIUrl":"https://doi.org/10.15625/1813-9663/36/1/13188","url":null,"abstract":"Aggregation of knowledge bases in the propositional language was soon investigated and the requirements of aggregation processes of propositional knowledge bases basically are unified within the community of researchers and applicants. Aggregation of standard possibilistic knowledge bases where the weight of propositional formulas being numeric has also been investigated and applied in building the intelligent systems, in multi-criterion decision-making processes as well as in decisionmaking processes implemented by many people. Symbolic possibilistic logic (SPL for short) where the weight of the propositional formulas is symbols was proposed, and recently it was proven that SPL is soundness and completeness. In order to apply SPL in building intelligent systems as well as in decision-making processes, it is necessary to solve the problem of aggregation of symbolic possibilistic knowledge bases (SPK bases for short). This problem has not been researched so far. The purpose of this paper is to investigate aggregation processes of SPK bases from the postulate point of view in propositional language. These processes are implemented via impossibility distributions defined from SPK bases. Characteristics of merging operators, including hierarchical merging operators, of symbolic impossibility distributions (SIDs for short) from the postulate point of view will be shown in the paper.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"5 1","pages":"17-32"},"PeriodicalIF":0.0,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90288510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.15625/1813-9663/35/4/14123
C. Le, V. Vu, L. K. Oanh, Nguyen Thi Hai Yen
Though clustering algorithms have long history, nowadays clustering topic still attracts a lot of attention because of the need of efficient data analysis tools in many applications such as social network, electronic commerce, GIS, etc. Recently, semi-supervised clustering, for example, semi-supervised K-Means, semi-supervised DBSCAN, semi-supervised graph-based clustering (SSGC) etc., which uses side information, has received a great deal of attention. Generally, there are two forms of side information: seed form (labeled data) and constraint form (must-link, cannot-link). By integrating information provided by the user or domain expert, the semi-supervised clustering can produce expected results. In fact, clustering results usually depend on side information provided, so different side information will produce different results of clustering. In some cases, the performance of clustering may decrease if the side information is not carefully chosen. This paper addresses the problem of efficient collection of seeds for semi-supervised clustering, especially for graph based clustering by seeding (SSGC). The properly collected seeds can boost the quality of clustering and minimize the number of queries solicited from the user. For this purpose, we have developed an active learning algorithm (called SKMMM) for the seeds collection task, which identifies candidates to solicit users by using the K-Means and min-max algorithms. Experiments conducted on real data sets from UCI and a real collected document data set show the effectiveness of our approach compared with other methods.
{"title":"CHOOSING SEEDS FOR SEMI-SUPERVISED GRAPH BASED CLUSTERING","authors":"C. Le, V. Vu, L. K. Oanh, Nguyen Thi Hai Yen","doi":"10.15625/1813-9663/35/4/14123","DOIUrl":"https://doi.org/10.15625/1813-9663/35/4/14123","url":null,"abstract":"Though clustering algorithms have long history, nowadays clustering topic still attracts a lot of attention because of the need of efficient data analysis tools in many applications such as social network, electronic commerce, GIS, etc. Recently, semi-supervised clustering, for example, semi-supervised K-Means, semi-supervised DBSCAN, semi-supervised graph-based clustering (SSGC) etc., which uses side information, has received a great deal of attention. Generally, there are two forms of side information: seed form (labeled data) and constraint form (must-link, cannot-link). By integrating information provided by the user or domain expert, the semi-supervised clustering can produce expected results. In fact, clustering results usually depend on side information provided, so different side information will produce different results of clustering. In some cases, the performance of clustering may decrease if the side information is not carefully chosen. This paper addresses the problem of efficient collection of seeds for semi-supervised clustering, especially for graph based clustering by seeding (SSGC). The properly collected seeds can boost the quality of clustering and minimize the number of queries solicited from the user. For this purpose, we have developed an active learning algorithm (called SKMMM) for the seeds collection task, which identifies candidates to solicit users by using the K-Means and min-max algorithms. Experiments conducted on real data sets from UCI and a real collected document data set show the effectiveness of our approach compared with other methods.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79450477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.15625/1813-9663/35/4/13898
B. Cuong
Picture fuzzy set (2013) is a generalization of the Zadeh‟ fuzzy set (1965) and the Antanassov‟intuitionistic fuzzy set. The new concept could be useful for many computational intelligentproblems. Basic operators of the picture fuzzy logic were studied by Cuong, Ngan [10,11 ].Newconcept –Pythagorean picture fuzzy set ( PPFS) is a combination of Picture fuzzy set with theYager‟s Pythagorean fuzzy set [12-14].First, in the Part 1 of this paper, we consider basic notionson PPFS as set operators of PPFS‟s , Pythagorean picture relation, Pythagorean picture fuzzy softset. Next, the Part 2 of the paper is devoted to main operators in fuzzy logic on PPFS: picturenegation operator, picture t-norm, picture t-conorm, picture implication operators on PPFS.As aresult we will have a new branch of the picture fuzzy set theory.
{"title":"Pythagorean Picture Fuzzy Sets, Part 1- basic notions","authors":"B. Cuong","doi":"10.15625/1813-9663/35/4/13898","DOIUrl":"https://doi.org/10.15625/1813-9663/35/4/13898","url":null,"abstract":"Picture fuzzy set (2013) is a generalization of the Zadeh‟ fuzzy set (1965) and the Antanassov‟intuitionistic fuzzy set. The new concept could be useful for many computational intelligentproblems. Basic operators of the picture fuzzy logic were studied by Cuong, Ngan [10,11 ].Newconcept –Pythagorean picture fuzzy set ( PPFS) is a combination of Picture fuzzy set with theYager‟s Pythagorean fuzzy set [12-14].First, in the Part 1 of this paper, we consider basic notionson PPFS as set operators of PPFS‟s , Pythagorean picture relation, Pythagorean picture fuzzy softset. Next, the Part 2 of the paper is devoted to main operators in fuzzy logic on PPFS: picturenegation operator, picture t-norm, picture t-conorm, picture implication operators on PPFS.As aresult we will have a new branch of the picture fuzzy set theory.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81436340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.15625/1813-9663/35/4/14131
Bac Le, L. Kieu, Dat Tran
In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss.
{"title":"DISTORTION-BASED HEURISTIC METHOD FOR SENSITIVE ASSOCIATION RULE HIDING","authors":"Bac Le, L. Kieu, Dat Tran","doi":"10.15625/1813-9663/35/4/14131","DOIUrl":"https://doi.org/10.15625/1813-9663/35/4/14131","url":null,"abstract":"In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78644209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.15625/1813-9663/35/4/13648
Trương Hà Hải, Vu Vinh Quang, D. Long
In the recent works, Dang and Truong proposed an iterative method for solving some problems of plates on one, two and three line partial internal supports (LPISs), and a cross internal support. In nature they are problems with strongly mixed boundary conditions for biharmonic equation. For this reason the method combines a domain decomposition technique with the reduction of the order of the equation from four to two. In this study, the method is developed for plates on internal supports of more complex configurations. Namely, we examine the cases of symmetric rectangular and H-shape supports, where the computational domain after reducing to the first quadrant of the plate is divided into three subdomains. Also, we consider the case of asymmetric rectangular support where the computational domain needs to be divided into 9 subdomains. The problems under consideration are reduced to sequences of weak mixed boundary value problems for the Poisson equation, which are solved by difference method. The performed numerical experiments show the effectiveness of the iterative method.
{"title":"Numerical solution of the problems for plates on some complex partial internal supports","authors":"Trương Hà Hải, Vu Vinh Quang, D. Long","doi":"10.15625/1813-9663/35/4/13648","DOIUrl":"https://doi.org/10.15625/1813-9663/35/4/13648","url":null,"abstract":"In the recent works, Dang and Truong proposed an iterative method for solving some problems of plates on one, two and three line partial internal supports (LPISs), and a cross internal support. In nature they are problems with strongly mixed boundary conditions for biharmonic equation. For this reason the method combines a domain decomposition technique with the reduction of the order of the equation from four to two. In this study, the method is developed for plates on internal supports of more complex configurations. Namely, we examine the cases of symmetric rectangular and H-shape supports, where the computational domain after reducing to the first quadrant of the plate is divided into three subdomains. Also, we consider the case of asymmetric rectangular support where the computational domain needs to be divided into 9 subdomains. The problems under consideration are reduced to sequences of weak mixed boundary value problems for the Poisson equation, which are solved by difference method. The performed numerical experiments show the effectiveness of the iterative method.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88962457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.15625/1813-9663/35/4/13907
Hoa Nguyen
In this paper, we propose a new probabilistic relational database model, denote by PRDB, as an extension of the classical relational database model where the uncertainty of relational attribute values and tuples are respectively represented by finite sets and probability intervals. A probabilistic interpretation of binary relations on finite sets is proposed for the computation of their probability measures. The combination strategies on probability intervals are employed to combine attribute values and compute uncertain membership degrees of tuples in a relation. The fundamental concepts of the classical relational database model are extended and generalized for PRDB. Then, the probabilistic relational algebraic operations are formally defined accordingly in PRDB. In addition, a set of the properties of the algebraic operations in this new model also are formulated and proven.
{"title":"EXTENDING RELATIONAL DATABASE MODEL FOR UNCERTAIN INFORMATION","authors":"Hoa Nguyen","doi":"10.15625/1813-9663/35/4/13907","DOIUrl":"https://doi.org/10.15625/1813-9663/35/4/13907","url":null,"abstract":"In this paper, we propose a new probabilistic relational database model, denote by PRDB, as an extension of the classical relational database model where the uncertainty of relational attribute values and tuples are respectively represented by finite sets and probability intervals. A probabilistic interpretation of binary relations on finite sets is proposed for the computation of their probability measures. The combination strategies on probability intervals are employed to combine attribute values and compute uncertain membership degrees of tuples in a relation. The fundamental concepts of the classical relational database model are extended and generalized for PRDB. Then, the probabilistic relational algebraic operations are formally defined accordingly in PRDB. In addition, a set of the properties of the algebraic operations in this new model also are formulated and proven.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89250097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.15625/1813-9663/35/4/14348
Phạm Đình Phong, Nguyen Duc Du, N. Thuy, Hoàng Văn Thông
During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results.
{"title":"A HEDGE ALGEBRAS BASED CLASSIFICATION REASONING METHOD WITH MULTI-GRANULARITY FUZZY PARTITIONING","authors":"Phạm Đình Phong, Nguyen Duc Du, N. Thuy, Hoàng Văn Thông","doi":"10.15625/1813-9663/35/4/14348","DOIUrl":"https://doi.org/10.15625/1813-9663/35/4/14348","url":null,"abstract":"During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85726411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-15DOI: 10.15625/1813-9663/35/3/13277
Thanh Do Van, Phuong Truong Duc
{"title":"FUZZY COMMON SEQUENTIAL RULES MINING IN QUANTITATIVE SEQUENCE DATABASES","authors":"Thanh Do Van, Phuong Truong Duc","doi":"10.15625/1813-9663/35/3/13277","DOIUrl":"https://doi.org/10.15625/1813-9663/35/3/13277","url":null,"abstract":"","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"131 1","pages":"217-232"},"PeriodicalIF":0.0,"publicationDate":"2019-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88351737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-15DOI: 10.15625/1813-9663/0/0/13277
Thanh Do Van, Phuong Truong Duc
Common Sequential Rules present a relationship between unordered itemsets in which the items in antecedents have to appear before ones in consequents. The algorithms proposed to find the such rules so far are only applied for transactional sequence databases, not applied for quantitative sequence databases.The goal of this paper is to propose a new algorithm for finding the fuzzy common sequential (FCS for short) rules in quantitative sequence databases. The proposed algorithm is improved by basing on the ERMiner algorithm. It is considered to be the most effective today compared to other algorithms for finding common sequential rules in transactional sequence database. FCS rules are more general than classical fuzzy sequential rules and are useful in marketing, market analysis, medical diagnosis and treatment
{"title":"FUZZY COMMON SEQUENTIAL RULES MINING IN QUANTITATIVE SEQUENCE DATABASES","authors":"Thanh Do Van, Phuong Truong Duc","doi":"10.15625/1813-9663/0/0/13277","DOIUrl":"https://doi.org/10.15625/1813-9663/0/0/13277","url":null,"abstract":"Common Sequential Rules present a relationship between unordered itemsets in which the items in antecedents have to appear before ones in consequents. The algorithms proposed to find the such rules so far are only applied for transactional sequence databases, not applied for quantitative sequence databases.The goal of this paper is to propose a new algorithm for finding the fuzzy common sequential (FCS for short) rules in quantitative sequence databases. The proposed algorithm is improved by basing on the ERMiner algorithm. It is considered to be the most effective today compared to other algorithms for finding common sequential rules in transactional sequence database. FCS rules are more general than classical fuzzy sequential rules and are useful in marketing, market analysis, medical diagnosis and treatment","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89345564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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