Feature Space Unidimensional Projections for Scatterplots

Eler, Danilo Medeiros; Almeida, Alex Castilho de; Teixeira, Jaqueline; Pola, Ives Renê Venturini; Pola, Fernanda Paula Barbosa; Dias, Maurício Araújo; Olivete, Celso

doi:10.5747/ce.2017.v09.n1.e184

Cited by 2 publications

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“…In the literature, multidimensional projections have been used to support the exploration of multidimensional datasets from different domains [14,[18][19][20][21]. Those techniques are commonly employed to reduce the dataset dimensionality to a lower dimensionality dataset.…”

Section: Multidimensional Projectionmentioning

confidence: 99%

Analysis of Document Pre-Processing Effects in Text and Opinion Mining

et al. 2018

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Typically, textual information is available as unstructured data, which require processing so that data mining algorithms can handle such data; this processing is known as the pre-processing step in the overall text mining process. This paper aims at analyzing the strong impact that the pre-processing step has on most mining tasks. Therefore, we propose a methodology to vary distinct combinations of pre-processing steps and to analyze which pre-processing combination allows high precision. In order to show different combinations of pre-processing methods, experiments were performed by comparing some combinations such as stemming, term weighting, term elimination based on low frequency cut and stop words elimination. These combinations were applied in text and opinion mining tasks, from which correct classification rates were computed to highlight the strong impact of the pre-processing combinations. Additionally, we provide graphical representations from each pre-processing combination to show how visual approaches are useful to show the processing effects on document similarities and group formation (i.e., cohesion and separation).

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Section: Multidimensional Projectionmentioning

confidence: 99%

Analysis of Document Pre-Processing Effects in Text and Opinion Mining

et al. 2018

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“…Some visualization techniques can be employed to present the dataset instances' similarities in 2D space; thus, the groups are naturally created according to the feature space that describes the dataset. Usually, multidimensional projection techniques [1][2][3] are employed in this visualization process by reducing the dataset dimensionality to two dimensions.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Visualization Approach to Show Documents Similarity and Content in a Single View

2018

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Multidimensional projection techniques can be employed to project datasets from a higher to a lower dimensional space (e.g., 2D space). These techniques can be used to present the relationships of dataset instances based on distance by grouping or separating clusters of instances in the projected space. Several works have used multidimensional projections to aid in the exploration of document collections. Even though the projection techniques can organize a dataset, the user needs to read each document to understand the cluster generation. Alternatively, techniques such as topic extraction or tag clouds can be employed to present a summary of the document contents. To minimize the exploratory work and to aid in cluster analysis, this work proposes a new hybrid visualization to show both document relationship and content in a single view, employing multidimensional projections to relate documents and tag clouds. We show the effectiveness of the proposed approach in the exploration of two document collections composed by world news.

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Feature Space Unidimensional Projections for Scatterplots

Cited by 2 publications

References 0 publications

Analysis of Document Pre-Processing Effects in Text and Opinion Mining

Analysis of Document Pre-Processing Effects in Text and Opinion Mining

Hybrid Visualization Approach to Show Documents Similarity and Content in a Single View

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