Clustering feature vectors with mixed numerical and categorical attributes

Brouwer, Roelof K.

doi:10.1080/18756891.2008.9727625

Cited by 10 publications

(16 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There exists a large amount of literature involving the ranking of fuzzy numbers [1][2][3][4][5][6][7][8][9] . Roughly speaking, a fuzzy number may be considered as a representation for an ill-known quantity.…”

Section: Introductionmentioning

confidence: 99%

A Ranking Method of Triangular Intuitionistic Fuzzy Numbers and Application to Decision Making

Li¹,

Nan²,

Zhang³

2010

International Journal of Computational Intelligence Systems

View full text Add to dashboard Cite

Ranking of triangular intuitionistic fuzzy numbers (TIFNs) is an important problem, which is solved by the value and ambiguity based ranking method developed in this paper. Firstly, the concept of TIFNs is introduced. Arithmetic operations and cut sets over TIFNs are investigated. Then, the values and ambiguities of the membership degree and the non-membership degree for TIFNs are defined as well as the value-index and ambiguity-index. Finally, a value and ambiguity based ranking method is developed and applied to solve multiattribute decision making problems in which the ratings of alternatives on attributes are expressed using TIFNs. A numerical example is examined to demonstrate the implementation process and applicability of the method proposed in this paper. Furthermore, comparison analysis of the proposed method is conducted to show its advantages over other similar methods.

show abstract

Section: Introductionmentioning

confidence: 99%

A Ranking Method of Triangular Intuitionistic Fuzzy Numbers and Application to Decision Making

Li¹,

Nan²,

Zhang³

2010

International Journal of Computational Intelligence Systems

View full text Add to dashboard Cite

show abstract

“…A support vector machine (SVM) has been used to cluster the documents, however, any of a number of clustering methods could be applied to perform document similarity measures. The first stage of knowledge acquisition and reduction of complexity concerning a group of objects is to partition or divide the objects into groups based on their attributes or characteristics 26 . Document clustering is a form of unsupervised machine learning, which given a set of input documents, extracts features from the documents and groups the documents into clusters based on the presence or absence of the features.…”

Section: Document Clustering Techniquesmentioning

confidence: 99%

BMR: Benchmarking Metrics Recommender for Personnel issues in Software Development Projects

García-Crespo

Colomo‐Palacios

Gómez-Berbís

et al. 2009

International Journal of Computational Intelligence Systems

View full text Add to dashboard Cite

This paper presents an architecture which applies document similarity measures to the documentation produced during the phases of software development in order to generate recommendations of process and people metrics for similar projects. The application makes a judgment of similarity of the Service Provision Offer (SPO) document of a new proposed project to a collection of Project History Documents (PHD), stored in a repository of unstructured texts. The process is carried out in three stages: firstly, clustering of the Offer document with the set of PHDs which are most similar to it; this provides the initial indication of whether similar previous projects exist, and signifies similarity. Secondly, determination of which PHD in the set is most comparable with the Offer document, based on various parameters: project effort, project duration (time), project resources (members/size of team), costs, and sector(s) involved, indicating comparability of projects. The comparable parameters are extracted using the GATE Natural Language Processing architecture. Lastly, a recommendation of metrics for the new project is made, which is based on the transferability of the metrics of the most similar and comparable PHD extracted, here referred to as recommendation.

show abstract

“…Brouwer 48 summarizes many distance calculation methods. In our work, the vertex method will be used because of its simplicity.…”

Section: Multi-criteria Decision-making Using Fuzzy Topsismentioning

confidence: 99%

An Alternative Ranking Approach and Its Usage in Multi-Criteria Decision-Making

Tolga¹,

Kahraman²

2009

IJCIS

View full text Add to dashboard Cite

In the process of fuzzy decision-making, ranking of fuzzy numbers is a necessity. The types of fuzzy numbers are triangular, trapezoidal, and L-R type. In the literature, there are many methods developed for ranking fuzzy numbers. These methods may produce different ranking results. Many of these methods necessitate graphical representations, complex and tedious calculations. The method developed in this paper has some advantages with respect to the other methods in both graphical representations and calculations. Applicability of the proposed method to multi-criteria decision-making methods, i.e. fuzzy scoring, fuzzy AHP and fuzzy TOPSIS methods, is shown in the paper.

show abstract

Clustering feature vectors with mixed numerical and categorical attributes

Cited by 10 publications

References 37 publications

A Ranking Method of Triangular Intuitionistic Fuzzy Numbers and Application to Decision Making

A Ranking Method of Triangular Intuitionistic Fuzzy Numbers and Application to Decision Making

BMR: Benchmarking Metrics Recommender for Personnel issues in Software Development Projects

An Alternative Ranking Approach and Its Usage in Multi-Criteria Decision-Making

Contact Info

Product

Resources

About