New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping

Lamirel, Jean–Charles; François, Claire; Shehabi, Shadi Al; Hoffmann, Martial

doi:10.1023/b:scie.0000034386.05278.e8

Cited by 38 publications

(30 citation statements)

References 6 publications

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“…Table 1 summarizes the results of the indexing phase. Each of our experiments is initiated with optimal gases generated by means of an optimization algorithm based on our quality criteria [17] (see also Fig. 4).…”

Section: Resultsmentioning

confidence: 99%

“…Thanks to these two indexes, a clustering result is considered as good if it possesses low intra-cluster inertia as compared to its inter-cluster inertia. However, as it has been shown in [17], the distance based indexes are often strongly biased 2 and highly dependent on the clustering method. They cannot thus be easily used for comparing different methods, or even different clustering results issued from data whose description spaces have different sizes.…”

Section: Quality Of Classification Modelmentioning

confidence: 99%

“…To cope with such problems, our own approach takes its inspiration both from the behavior of symbolic classifiers and from the evaluation principles used in Information Retrieval. Our Recall/Precision and F-measures indexes exploit the properties of the data associated to each cluster after the clustering process without prior consideration of clusters profiles [17]. Their main advantage is thus to be independent of the clustering methods and of their operating mode.…”

Section: Quality Of Classification Modelmentioning

confidence: 99%

See 2 more Smart Citations

A New Multi-Viewpoint and Multi-Level Clustering Paradigm for Efficient Data Mining Tasks

Lamirel¹

2011

New Fundamental Technologies in Data Mining

Self Cite

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Section: Resultsmentioning

confidence: 99%

Section: Quality Of Classification Modelmentioning

confidence: 99%

Section: Quality Of Classification Modelmentioning

confidence: 99%

See 1 more Smart Citation

A New Multi-Viewpoint and Multi-Level Clustering Paradigm for Efficient Data Mining Tasks

Lamirel¹

2011

New Fundamental Technologies in Data Mining

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“…To overcome this dilemma, Lamiel and other [29]- [32] have proposed improvements of the subsequent methods (Recall, Precision and F-Measures) based on reference classification, by making them adequate and relevant to unsupervised classification.…”

Section: Functions Meaningmentioning

confidence: 99%

A Generic Methodology for Clustering to Maximises Inter-Cluster Inertia

Alaoui¹,

Ibara²,

Ettaki³

et al. 2017

ijacsa

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Abstract-This paper proposes a novel clustering methodology which undeniably manages to offer results with a higher inter-cluster inertia for a better clustering. The advantage obtained with this methodology is due to an algorithm that showed beforehand its efficiency in clustering exercises, MC-DBSCAN, which is associated to an iterative process with a potential of auto-adjustment of the weights of the pertinent criteria that allows the reclassification of objects of the two closest clusters through each iteration, as well as the aptitude of the auto-evaluation of the precision of the clustering during the clustering process. This work conducts the experiments using the well-known benchmark, 'Seismic', 'Landform-Identification' and 'Image Segmentation', to compare the performance of the proposed methodology with other algorithms (K-means, EM, CURE and MC-DBSCAN). The experimental results demonstrate that the proposed solution has good quality of clustering results.

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“…However, it has been shown in [12] that the distance based indexes are often strongly biased and highly dependent on the clustering method. They cannot thus be easily used for comparing different methods.…”

Section: Introductionmentioning

confidence: 99%

A New Efficient and Unbiased Approach for Clustering Quality Evaluation

Lamirel

Cuxac

Mall

et al. 2012

Lecture Notes in Computer Science

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To cite this version:Jean-Charles Lamirel, Pascal Cuxac, Raghvendra Mall. A new efficient and unbiased approach for clustering quality evaluation. QIMIE'11, May 2011, Shenzen, China. pp.209-220. hal-00955498 A new efficient and unbiased approach for clustering quality evaluation Abstract. Traditional quality indexes (Inertia, DB, . . . ) are known to be method-dependent indexes that do not allow to properly estimate the quality of the clustering in several cases, as in that one of complex data, like textual data. We thus propose an alternative approach for clustering quality evaluation based on unsupervised measures of Recall, Precision and F-measure exploiting the descriptors of the data associated with the obtained clusters. Two categories of index are proposed, that are Macro and Micro indexes. This paper also focuses on the construction of a new cumulative Micro precision index that makes it possible to evaluate the overall quality of a clustering result while clearly distinguishing between homogeneous and heterogeneous, or degenerated results. The experimental comparison of the behavior of the classical indexes with our new approach is performed on a polythematic dataset of bibliographical references issued from the PASCAL database.

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New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping

Cited by 38 publications

References 6 publications

A New Multi-Viewpoint and Multi-Level Clustering Paradigm for Efficient Data Mining Tasks

A New Multi-Viewpoint and Multi-Level Clustering Paradigm for Efficient Data Mining Tasks

A Generic Methodology for Clustering to Maximises Inter-Cluster Inertia

A New Efficient and Unbiased Approach for Clustering Quality Evaluation

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