Mining association rules for the quality improvement of the production process

Kamsu-Foguem, Bernard; Rigal, Fabien; Mauget, FéLix

doi:10.1016/j.eswa.2012.08.039

Cited by 132 publications

(54 citation statements)

References 56 publications

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“…This knowledge can provide evidence and analysis relevant to the activities of diagnoses and it is very important for medical research in geriatrics or neurosciences. To that end, we plan to include the engagement of reasoning techniques to determine by sequential association rules [37] the most significant rules to assist in the establishment of the most appropriate diagnosis and to eliminate differential diagnoses. Shared practice rules face the challenge of creating a solid foundation to support the cognitive development of collective intelligence.…”

Section: Visual Modelling Of Analysis Procedures In Synucleinopathiesmentioning

confidence: 99%

Knowledge-based modelling applied to synucleinopathies

Kamsu-Foguem

Tiako

Mutafungwa

et al. 2015

European Geriatric Medicine

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International audienceThe adoption of telemedicine technologies has enabled collaborative programs involving a variety of links among distributed medical structures and health officials and professionals. The use for telemedicine for transmission of medical data and the possibility for several distant physicians to share their knowledge on given medical cases provides clear benefits, but also raises several unsolved conceptual and technical challenges. The seamless exchange and access of medical information between medical structures, health professionals, and patients is a prerequisite for the harmonious development of this new medical practice. This paper proposes a new approach of semantic interoperability for enabling mutual understanding of terminologies and concepts used. The proposed semantic interoperability approach is based on conceptual graph to support collaborative activities by describing how different health specialists can apply appropriate strategies to eliminate differential medical diagnosis. Intelligent analysis strategies are used to narrow down and pinpoint medical disorders. The model proposed is fully verified by a case study in the context of elderly patients and specifically dealing with synucleinopathies, a group of neurodegenerative diseases that include Parkinson's disease (PD), dementia with Lewy bodies (DLB), pure autonomic failure (PAF) and multiple system atrophy (MSA)

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Section: Visual Modelling Of Analysis Procedures In Synucleinopathiesmentioning

confidence: 99%

Knowledge-based modelling applied to synucleinopathies

Kamsu-Foguem

Tiako

Mutafungwa

et al. 2015

European Geriatric Medicine

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“…However, the notion of using a CG to extract knowledge, as well as reason with it, is not new. Manipulation of knowledge graphs in real‐life applications has been researched in the past,() and as such, it stands to reason that usage of a CG in cognitive agents could enable a better human‐agent interaction, especially for knowledge transference .…”

Section: Introductionmentioning

confidence: 99%

Cognitive agents and machine learning by example: Representation with conceptual graphs

Gkiokas

Cristea

2018

Computational Intelligence

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As machine learning (ML) and artificial intelligence progress, more complex tasks can be addressed, quite often by cascading or combining existing models and technologies, known as the bottom‐up design. Some of those tasks are addressed by agents, which attempt to simulate or emulate higher cognitive abilities that cover a broad range of functions; hence, those agents are named cognitive agents. We formulate, implement, and evaluate such a cognitive agent, which combines learning by example with ML. The mechanisms, algorithms, and theories to be merged when training a cognitive agent to read and learn how to represent knowledge have not, to the best of our knowledge, been defined by the current state‐of‐the‐art research. The task of learning to represent knowledge is known as semantic parsing, and we demonstrate that it is an ability that may be attained by cognitive agents using ML, and the knowledge acquired can be represented by using conceptual graphs. By doing so, we create a cognitive agent that simulates properties of “learning by example,” while performing semantic parsing with good accuracy. Due to the unique and unconventional design of this agent, we first present the model and then gauge its performance, showcasing its strengths and weaknesses.

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“…Text mining also refers to as text data mining [1]- [4], roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. People usually use methods in data mining to find interesting information from text, such as the well-known data mining methods association rule mining [4]- [6], classification [7]- [8], clustering [9]- [10], etc.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Document Clustering Approach with Frequent Itemsets

Lee¹,

Hsu²,

Chen³

2017

IJET

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Abstract-In order to effectively retrieve required information from the large amount of information collected from the Internet, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document of collected information and require special handling for high dimensionality and high volume. We propose the OCFI (Ontology and Closed Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering. OCFI uses common words to cluster documents and builds hierarchical topic tree. In addition, OCFI utilizes ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results reveal that our method is more effective than the well-known document clustering algorithms. The clustering results can be used in the personalized search service to assist users to obtain the information they need.Index Terms-OCFI, documents clustering, ontology, closed frequent itemsets. I. INTRODUCTIONDue to the popularity of the Internet, a large number of documents, reports, e-mails, and web pages cause the information overload problem. Many enterprises spend lots of manpower on organizing these unstructured data into a logical structure for later use. In order to save the manpower and find interesting knowledge effectively, text mining becomes more and more important. information from text, text mining usually involves the process of parsing the input text, mining valuable information through the analysis models, and finally evaluating and interpreting the output. However, most of the text mining algorithms do not have enough capability to solve the problems from text mining, because many methods are modifications of traditional data mining algorithms that were originally designed for relational database. Therefore, traditional clustering algorithms become impractical in real-world document clustering which requires special handling for high dimensionality, high volume, and ease of browsing.Fung proposed Frequent Itemset-based Hierarchical Clustering (FIHC) [11] method to solve the problems from traditional algorithms. FIHC is a hierarchical clustering method developed for document clustering. Clustering or cluster analysis is the task of assigning a set of objects into groups, called clusters, so that the objects in the same cluster are more similar to each other than to those in other clusters. Clustering is a main topic of data mining algorithm, and a common technique for statistical data analysis used in many fields, including machine learning, image analysis, information retrieval, and bioinformatics.A major breakthrough of FIHC is that the clustering algorithm utilizes an important notion, frequent items...

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Mining association rules for the quality improvement of the production process

Cited by 132 publications

References 56 publications

Knowledge-based modelling applied to synucleinopathies

Knowledge-based modelling applied to synucleinopathies

Cognitive agents and machine learning by example: Representation with conceptual graphs

A Hierarchical Document Clustering Approach with Frequent Itemsets

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