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An ontology is a framework for describing domain-specific knowledge in a structured format. It is comprised of a set of terms as nodes and a set of relationships between terms as directed edges to form a directed acyclic graph. Gene Ontology (GO) and Human Phenotype Ontology (HPO) are widely referred biological and biomedical ontology databases. They also provide extensive annotations of human genes...
No Evidence of Disease (NED) is breast cancer patient condition status which it indicates that they can life, no find the cancer by tested, and without any symptoms of cancer in period of times, after they received primary treatment. NED is a critical status, because it involves the treatment type and patient cancer condition factors. This paper examines about breast cancer problem in data mining...
Data mining can find some interest information from large amounts of data. Data association (association rules) can find associations among data items. Data classification distinguishes every data from a data set or group, and it also can combine data association. Formal concept analysis is a data analyzing theory which discovers concept structure in data sets. It can transform formal context into...
For a large sum of data collected and stored continually, it is more and more necessary to mine association rules from database, and the Apriori algorithm of association rules mining is the most classical algorithm of database mining and is widely used. However, Apriori algorithm has some disadvantages such as low efficiency of candidate item sets and scanning data frequently. Support and confidence...
With the advent of the big data era, data mining technology has gradually become mature, association rules analysis is also applied in many fields. Web log mining is an important way to do some personalized services and achieve Web personalize. Apriori algorithm is a classical algorithm of association rules, but it has a lot of shortcomings. In recent years, the improvement about Apriori algorithm...
Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The goal of classification is to accurately predict the target class for each case in the data. In sequence database having sequences, in which each sequence is a list of...
Datamining is the process of extracting interesting information of patterns from large databases. One of the most important datamining task and well-researched is the association rules mining. It aims to find the interesting correlation and relations among sets of items in the transaction databases. One of the main problems related to the discovery of these associations that a decision maker faces...
Today, development of internet causes a fast growth of internet shops and retailers and makes them as a main marketing channel. This kind of marketing generates a numerous transaction and data which are potentially valuable. Using data mining is an alternative to discover frequent patterns and association rules from datasets. In this paper, we use data mining techniques for discovering frequent customers'...
With the technical advances, multimedia data is growing exponentially. It is progressively vital to mine the information from a video database consequently. Finding association between things in a large video assumes an extensive part in the video information mining. In light of the innovative work in the previous years, use of association mining is developing in various areas, for example, surveillance,...
The grant for the research gives the researcher the important opportunity to make fruitful research results. Recently, the notification of the government grants and some Foundation grants in the various fields informs the researchers through Internet. However, the notification provided through Internet includes ambiguous and complex. The researchers will fail to notice the grant information which...
Data mining applied on educational data aims to find useful patterns in large volumes of data in order to transform and optimize educational paths. It involves many steps. This paper presents a case study for a data preprocessing framework for students' outcome prediction using data collected by Moodle system.
Database constraints, such as "patients with the same symptoms get the same therapies", may be modeled by means of functional dependencies (FD). They have been extended to represent temporal constraints such as "patients with the same symptoms and the same administered therapies, receive in the next period the same therapies". These constraints are called temporal functional dependencies...
This article presents an ongoing investigation that uses mining tools to analyze a large volume of data from students of online courses in a LMS platform to discover Association Rules used to identifying dropout situations. These ARs are used along with several explicit (formalized) rules elicited from course operators and are intended to be used by a software agent to detect individuals within a...
Educational Data Mining (EDM) relates to the inter-disciplinary research that deals with the development of various methods and techniques to explore the data generated from different educational sources. EDM techniques investigate the data in the pursuit of answers to educational questions and unknown patterns which surface after the investigations. This survey paper pictures the evolution of EDM...
Bacterial colonies perform a cooperative distributed exploration of the environment. This paper describes bacterial colony networks and their skills to explore resources as a tool for mining association rules in databases. The proposed algorithm is designed to maintain diverse solutions to the problem at hand, and its performance is compared to other well-known bio-inspired algorithms, including a...
This paper presents an approach for building annotation rules for texts containing natural language descriptions of the endoscopies, in Romanian. The annotation rule extraction relies on running the Apriori algorithm over some previously annotated texts. It does not employ any Natural Language Processing tools. We show by experiments that, for the relatively small vocabulary employed by those descriptions,...
Temporal Functional Dependencies (TFDs for short) are functional dependencies that predicate on temporal databases characterized by a special temporal dimension called valid time (VT). In [1] Combi et al. proposed a uniform framework that subsumes many of the TFDs proposed in literature and, by the combination of them, allow us to express finer constraints. Some interesting constraints are the Temporally...
Data mining techniques is a popular research area. Association rule mining is the technique used to detect rules and patterns. One of the most well-known techniques is the Direct Hashing and Pruning (DHP) algorithm. This algorithm tries to find associations among the various data items in the date warehouse. In this paper, the attempt was made to optimize this algorithm further by changing its data...
Sequential pattern mining is valuable approach to uncover consumer buying behaviour from huge sequence database. Weather prediction, web log analysis, stock market analysis, scientific research, sales analysis, and so on are the application of sequential pattern mining. The pattern that is recent and profitable can't discover by conventional sequential pattern mining. So, RFM-based sequential pattern...
Radio Frequency Identification (RFID) is a useful ICT technology for E-logistics Enterprises. One of the standards used for RFID is Electronic Product Code Information Services (EPCIS). However, it is nontrivial to get effective knowledge from massive data to improve the existed production or logistic system comparing with convenient data collection. In this paper, we develop an intelligent platform...
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