Fuzzy clustering of web documents using equivalence relations and fuzzy hierarchical clustering

Kumar, Sanjeev; Kathuria, Mamta; Gupta, Alok; Rani, Manju

doi:10.1109/conseg.2012.6349496

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Clustering is defined as the process of organizing objects into groups whose elements are homogenous in some way and that is called segment or cluster. A cluster is a collection of objects which are similar between them and are different to the objects belonging to other clusters [33]. The object that is not part of any cluster is called outlier.…”

Section: Clusteringmentioning

confidence: 99%

Data Mining Technology and its Applications to Power Systems

Mishra¹,

Saxena²

2015

IJCA

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With increasingly environmental constraints the modern Power and Energy Systems are experiencing huge transformations in many ways. The quantum of data in power systems is growing rapidly due to large database used by power systems engineers for various operations. From the power generation plants the electrical energy is transmitted and distributed to end users. Frequent failure of various equipments and the systems has made it impossible to maintain the continuity of supply. Sometimes these failures are beyond the control of the power system operator. The operation and planning of power systems provide a large amount of data and it is difficult to extract the useful information from this large database that is continuously used by operators. Data mining is a process of extracting interesting and previously unknown knowledge from a set of data. The data mining techniques help power systems planner/operator to have smooth system planning, operation and are useful for extracting useful information from the existing data banks. The paper describes the data mining technology and its applications that would be useful in power systems.

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Section: Clusteringmentioning

confidence: 99%

Data Mining Technology and its Applications to Power Systems

Mishra¹,

Saxena²

2015

IJCA

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“…At the same time, in the application process of computer technology, people generally attach importance to computer software, but they do not attach enough importance to the automation management of software engineering, neglecting to take effective measures to improve and enhance, affecting the full play of the role of computer software [3]. In this context, we should change our mindset, recognize the important role of automation management, and effectively integrate it into computer software engineering projects [4]. As an important part of the development of computer technology today, computer software development has ushered in a series of new problems while achieving excellent results.…”

Section: Introductionmentioning

confidence: 99%

Research on Computer Software Engineering Project Automation Management based on Data Mining and Fuzzy Clustering

Zhang¹

2019

Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

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The smooth implementation of automation management of computer software engineering projects has played an important role in promoting the faster development of computer software development and further promoting the development of computer software engineering. In the process of realizing automation management of project, data mining technology and method are introduced, and a computer aided quality function configuration model based on data mining is proposed. The data mining tool was developed, and the model for product user demand and process quality selection decomposition was established. At the same time, the data mining model of fuzzy clustering and grey theory algorithm was established, and related application examples were analyzed. At present, all methods of validity discrimination do not have absolute advantages, and all need to be adapted and selected for specific data sets.

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“…The FEC algorithm has been applied in a variety of fields, such as information retrieval, knowledge discovery, data mining, etc. [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems

Liu

2016

Knowledge-Based Systems

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In order to deal with the fuzzy large-scale multiple-criteria group decision-making (FLMCGDM) problems, this paper incorporates clustering analysis and information aggregation operator into the problems of large-scale multiple-criteria group decision-making with interval type-2 fuzzy sets (IT2 FSs). The interval type-2 fuzzy equivalence clustering (IT2-FEC) analysis is used to classify decision-makers (DMs) to reduce the dimension of the large-scale DMs in the FLMCGDM problems. The combined weighted geometric averaging (CWGA) operator is extended into the case with IT2 FSs variables, which can take both the importance of individual and its relative position into account. Afterwards, a solution process for the FLMCGDM problems is proposed, in which the new equivalence clustering method and CWGA operator of IT2 FSs is incorporated. Finally, the reasonability and effectiveness of the proposed method are verified by an illustrative example. Compared with other methods, the IT2-FEC analysis can deal with the linguistic variables and produce dynamic clustering results in a more efficient way.

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Fuzzy clustering of web documents using equivalence relations and fuzzy hierarchical clustering

Cited by 7 publications

References 14 publications

Data Mining Technology and its Applications to Power Systems

Data Mining Technology and its Applications to Power Systems

Research on Computer Software Engineering Project Automation Management based on Data Mining and Fuzzy Clustering

An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems

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