Incremental board: a grid-based space for visualizing dynamic data sets
Proceedings of the 2009 ACM symposium on Applied Computing
An incremental space to visualize dynamic data sets
Multimedia Tools and Applications
Visualization of text streams: a survey
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
EASE'10 Proceedings of the 14th international conference on Evaluation and Assessment in Software Engineering
Time-aware visualization of document collections
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Semantic Wordification of Document Collections
Computer Graphics Forum
Seeing beyond reading: a survey on visual text analytics
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
An environmental search engine based on interactive visual classification
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Piecewise laplacian-based projection for interactive data exploration and organization
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
A framework for exploring multidimensional data with 3D projections
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Cluster ensemble selection based on relative validity indexes
Data Mining and Knowledge Discovery
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The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analysis of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations compute the coordinates of a set of projected points based on the coordinates of a reduced number of control points with defined geometry. We name the technique Least Square Projections (LSP). From an initial projection of the control points, LSP defines the positioning of their neighboring points through a numerical solution that aims at preserving a similarity relationship between the points given by a metric in $mD$. In order to perform the projection, a small number of distance calculations is necessary and no repositioning of the points is required to obtain a final solution with satisfactory precision. The results show the capability of the technique to form groups of points by degree of similarity in $2D$. We illustrate that capability through its application to mapping collections of textual documents from varied sources, a strategic yet difficult application. LSP is faster and more accurate than other existing high quality methods, particularly where it was mostly tested, that is, for mapping text sets.