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M.: Fuzzy functional dependency and its application to approximate data querying
 IDEAS, IEEE Computer Society
, 2000
"... In this paper, we review a new definition of fuzzy functional dependency based on conditional probability and its application to approximate data reduction related to operation of projection in classical relational database in order to construct fuzzy integrity constraints [l]. We introduce the co ..."
Abstract

Cited by 8 (2 self)
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In this paper, we review a new definition of fuzzy functional dependency based on conditional probability and its application to approximate data reduction related to operation of projection in classical relational database in order to construct fuzzy integrity constraints [l]. We introduce the concept of partial fuzzy functional dependency which expresses the fact that a given attribute X does not determine Y completely, but in the partial area of X, it might determine Y. Finally, we discuss another application of fuzzy functional dependency an constructing fuzzy query relation for data querying and approximate join of two or more fuzzy query relations in the framework of extended query system. 1.
Mining Multidimensional Fuzzy Association Rules from a Normalized Database
 International Conference on Convergence and Hybrid Information Technology, Daejeon
, 2008
"... ABSTRACT: Mining association rules is one of the important tasks in the process of data mining application. In general, the input as used in the process of generating rules is taken from a certain data table by which all the corresponding values of every domain data have correlations one to each oth ..."
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Cited by 5 (0 self)
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ABSTRACT: Mining association rules is one of the important tasks in the process of data mining application. In general, the input as used in the process of generating rules is taken from a certain data table by which all the corresponding values of every domain data have correlations one to each others as given in the table. A problem arises when we need to generate the rules expressing the relationship between two or more domains that belong to several different tables in a normalized database. To overcome the problem, before generating rules it is necessary to join the participant tables into a general table by a process called Denormalization Process. This paper shows a process of generating Multidimensional Fuzzy Association Rules mining from a normalized database of medical record patients. The process consists of two subprocesses, namely subprocess of join tables (Denormalization Process) and subprocess of generating fuzzy rules. In general, the process of generating the fuzzy rules has been discussed in our previous papers [1, 2, 3, 4]. In addition to the process of generating fuzzy rules, this paper proposes a correlation measure of the rules as an additional consideration for evaluating interestingness of provided rules.
DATA MINING APPLICATION FOR ANALYZING PATIENT TRACK RECORD USING DECISION TREE INDUCTION APPROACH
"... Decision Tree Induction (DTI), one of the data mining classification methods, is used in this research for predictive problem solving. We extend the concept of DTI dealing with meaningful fuzzy labels in order to express human knowledge. A user can generate a meaningful fuzzy label (using fuzzy set) ..."
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Decision Tree Induction (DTI), one of the data mining classification methods, is used in this research for predictive problem solving. We extend the concept of DTI dealing with meaningful fuzzy labels in order to express human knowledge. A user can generate a meaningful fuzzy label (using fuzzy set) for describing a condition/type of disease, such as poor disease, moderate disease, and severe disease. We employ the highest information gain to split a node. To reduce the generated rules we pay attention on minimum support and minimum confidence. In this paper, we present the usage of DTI to analyze patient track record. The designed application gives a significant contribution to assist decision maker for analyzing and anticipating disease epidemic in a certain area.
Aplikasi Steganografi pada Video dengan Teknik Least Significant Bit dan Gabungan Enkripsi Rivest Chiper 4
"... ABSTRAK: Perkembangan teknologi komunikasi dan informasi berkembang dengan pesat. Hal ini mempengaruhi kehidupan manusia dalam proses pertukaran informasi yang semakin mudah. Namun seiring dengan perkembangan tersebut, kejahatan dalam bidang ini pun turut berkembang, sehingga pertukaran informasi me ..."
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ABSTRAK: Perkembangan teknologi komunikasi dan informasi berkembang dengan pesat. Hal ini mempengaruhi kehidupan manusia dalam proses pertukaran informasi yang semakin mudah. Namun seiring dengan perkembangan tersebut, kejahatan dalam bidang ini pun turut berkembang, sehingga pertukaran informasi menjadi kurang aman. Aplikasi yang dibuat menggunakan bahasa pemrograman C# dengan bantuan library untuk melakukan proses terhadap file video yang digunakan. Untuk menyembunyikan file kedalam video menggunakan teknik Least Significant Bit dengan terlebih dahulu menambah keamanan pada file dengan cara melakukan enkripsi Rivest Chiper 4. Hasil akhir dari aplikasi ini adalah sebuah video yang berisikan data rahasia. Untuk mengekstrak data tersebut dibutuhkan password yang digunakan saat menyembunyikan data. Kemampuan aplikasi ini terbatas pada kemampuan library yang digunakan.
3Department of Informatics Engineering,
"... Abstract: Traditionally, similarity between two objects is calculated by using only their attribute values, such as number of coincided attributes, Euclid distance, etc. A new concept of similarity dealing with a uniqueness measure is proposed in this paper by which the similarity between two object ..."
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Abstract: Traditionally, similarity between two objects is calculated by using only their attribute values, such as number of coincided attributes, Euclid distance, etc. A new concept of similarity dealing with a uniqueness measure is proposed in this paper by which the similarity between two objects is not only considered using their attribute values, but also a subset of objects as an important parameter. Here, the subset of objects may be regarded as knowledge of human. In this concept of similarity, if attribute values of two objects are rare in the subset, and their attribute values are the same, then their degree of similarity is high. On the other hand, if the attribute values of two objects are not rare in the subset, and their attribute values are the same, then their degree of similarity is low. Consequently, the degree of similarity between two objects will be changed depending on the subset of objects. Moreover, we discuss mathematical properties of the concept of similarity dealing with uniqueness measure. Finally, we discuss differences between the concept of similarity based on uniqueness measure and traditional similarity using some examples.
Hybrid Probabilistic Models of Fuzzy and Rough Events
"... This paper discusses the relationship between probability and fuzziness based on the process of perception. As a generalization of crisp set, fuzzy set is used to model fuzzy event as proposed by Zadeh. Similarly, we may consider rough set to represent rough event in terms of probability measure. Sp ..."
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This paper discusses the relationship between probability and fuzziness based on the process of perception. As a generalization of crisp set, fuzzy set is used to model fuzzy event as proposed by Zadeh. Similarly, we may consider rough set to represent rough event in terms of probability measure. Special attention will be given to conditional probability of fuzzy event as well as conditional probability of rough event. Their several combinations of formulation and properties are examined. In the relation to evidence theory, the probability of rough event may be considered as a connecting bridge between beliefplausibility measures and the probability measures. Moreover, generalized fuzzyrough event is introduced to generalize both fuzzy and rough events.
3Center of Soft Computing and Intelligent System Studies,
"... In 2003, we proposed uniquenessbased similarity relationship between objects in a binary information system. The concept of similarity is based on human’s perception in which the degree of similarity between two objects is calculated based on unique value of attribute in a subset of objects. We ca ..."
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In 2003, we proposed uniquenessbased similarity relationship between objects in a binary information system. The concept of similarity is based on human’s perception in which the degree of similarity between two objects is calculated based on unique value of attribute in a subset of objects. We called the subset of objects as knowledge. Unique characteristic of uniquenessbased similarity is that similarity between two objects may have different degrees of similarity depending on which knowledge used as reference. In this paper, we extend the concept of uniquenessbased similarity by applying the concept into a fuzzy information system.
An Algorithm for Generating Single Dimensional Fuzzy Association Rule Mining
"... Association rule mining searches for interesting relationship among items in a large data set. Market basket analysis, a typical example of association rule mining, analyzes buying habit of customers by finding association between the different items that customers put in their shopping cart (basket ..."
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Association rule mining searches for interesting relationship among items in a large data set. Market basket analysis, a typical example of association rule mining, analyzes buying habit of customers by finding association between the different items that customers put in their shopping cart (basket). Apriori algorithm is an influential algorithm for mining frequent itemset for generating association rules. For some reasons, Apriori algorithm is not based on human intuitive. To provide a more humanbased concept, this paper proposes an alternative algorithm for generating the association rule by utilizing fuzzy sets in the market basket analysis. 1
Predicting Interval Probability in Data Querying
 Proceedings of IMECS 2007
, 2007
"... Abstract—This paper discusses fuzzification of crisp domains into fuzzy classes providing fuzzy domains. Relationship between two fuzzy domains, Xi and Xj, is represented by a matrix, wij. If Xi and Xj have n and m elements of fuzzy data, respectively, then wij is n × m matrix. Our primary goal in t ..."
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Cited by 1 (1 self)
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Abstract—This paper discusses fuzzification of crisp domains into fuzzy classes providing fuzzy domains. Relationship between two fuzzy domains, Xi and Xj, is represented by a matrix, wij. If Xi and Xj have n and m elements of fuzzy data, respectively, then wij is n × m matrix. Our primary goal in this paper is to generate and provide some formulas for predicting interval probability in the relation to data querying, i.e., given John is 30 years old and he has MS degree, what is his probability to getting high salary.
Interval Probability of Data Querying Based on Fuzzy Conditional Probability Relation ∗
"... Abstract—This paper discusses fuzzification of crisp domains into fuzzy classes providing fuzzy domains. Relationship between two fuzzy domains, Xi and Xj, is represented by a matrix, wij. If Xi and Xj have n and m elements of fuzzy data, respectively, then wij is n × m matrix. The primary goal of t ..."
Abstract
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Abstract—This paper discusses fuzzification of crisp domains into fuzzy classes providing fuzzy domains. Relationship between two fuzzy domains, Xi and Xj, is represented by a matrix, wij. If Xi and Xj have n and m elements of fuzzy data, respectively, then wij is n × m matrix. The primary goal of the paper is to generate and provide some formulas for predicting interval probability in the relation to data querying, i.e., given John is 30 years old and he has MS degree, what is his probability to getting high salary.
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