ions, such as functions and methods, are an essential tool for any programmer. Abstractions encapsulate the details of a computation: the programmer only needs to know what the abstraction achieves, not how it achieves it. However, using abstractions can come at a cost: the resulting program may be inefficient. This can lead to programmers not using some abstractions, instead writing the entire functionality from the ground up. In this thesis, we present several results that make this situation less likely when programming interactive visualizations. We present results that make abstractions more efficient in the areas of graphics, layout and events.

Efficient abstractions for visualization and interaction

The well-known Reingold-Tilford algorithm produces tidy-layered drawings of trees: drawings where all nodes at the same depth are vertically aligned. However, when nodes have varying heights, layered drawing may use more vertical space than necessary. A non-layered drawing of a tree places children at a fixed distance from the parent, thereby giving a more vertically compact drawing. Moreover, non-layered drawings can also be used to draw trees where the vertical position of each node is given, by adding dummy nodes. In this paper, we present the first linear-time algorithm for producing non-layered drawings. Our algorithm is a modification of the Reingold-Tilford algorithm, but the original complexity proof of the Reingold-Tilford algorithm uses an invariant that does not hold for the non-layered case. We give an alternative proof of the algorithm and its extension to non-layered drawings. To improve drawings of trees of unbounded degree, extensions to the Reingold-Tilford algorithm have been proposed. These extensions also work in the non-layered case, but we show that they then cause a On2 run-time. We then propose a modification to these extensions that restores the On run-time. Copyright © 2013 John Wiley & Sons, Ltd.

Drawing non-layered tidy trees in linear time

This is a survey of recent researches on 2D tree visualization approaches. The whole paper will focus on 2D layouts for general trees including different styles of node-link diagram, main variations of Treemap, some solutions designed especially for mobile devices, and several hybrid approaches. We are not trying to give an exhaustive list of tree layouts here. Instead, we will introduce new ideas in the main categories of general tree layouts, which may inspire more efficient and creative designs.

/pdf/2-d-layout-for-tree-visualization-a-survey-3s5hzeeyol.pdf

2-D Layout for Tree Visualization: a survey

Clustering is the process of locating patterns in large data sets. As databases continue to grow in size, efficient and effective clustering algorithms play a paramount role in data mining applications. Traditional clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed, but many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. Re-clustering the whole dataset from scratch is not a good choice due to the frequent data modifications and the limited out-of-service time, so the development of incremental clustering approaches is highly desirable. In this paper, we propose an incremental algorithm, IPYRAMID: Incremental Parallel hYbrid clusteRing using genetic progrAmming and Multiobjective fItness with Density employs a combination of data parallelism, genetic programming (GP), special operators, and multi-objective density-based incremental fitness function. Although many incremental clustering algorithms have been proposed which can handle insertion of new record properly using incremental approach but cannot handle deletion of record properly. This issue is resolved in the proposed algorithm and density based incremental fitness function that helps to handle outliers. Use of parallelism increases the speed of execution as well as identifies clusters of arbitrary shapes. The incremental merge engine can dynamically determine the number of clusters. Preliminary experimental results show that it can increase the efficiency of clustering process.

/pdf/incremental-cluster-detection-using-a-soft-computing-evox7r0725.pdf

Incremental Cluster Detection using a Soft Computing Approach

The emerging technologies and data centric applications have been becoming an integral part of business intelligence, decision process and numerous daily activities. To enable efficient pattern classification and data analysis, clustering has emerged as a potential mechanism that classifies data elements based on respective feature homogeneity. Although K-Means clustering has exhibited appreciable performance for data clustering, it suffers to enable optimal classification with high dimensional data sets. Numerous optimization efforts including genetic algorithm (GA) based clustering also require further optimization to avoid local minima issues. In this paper, an improved Canonical GA based Bisecting K-Means algorithm (CGABC) has been developed. The proposed model incorporates min-max normalization based feature normalization of the high dimensional data sets, which is followed by T-Test analysis that significantly reduces data dimensions based on feature similarity of the data elements. The fitness value has been assigned based on inter-cluster (heterogeneous distance) and within-cluster (homogeneous distance) distances. To enable optimal features and process parameter selection, particularly cluster centers information, the conventional GA has been modified by applying multistage reproduction process, enhanced crossover and mutation. By incorporating the optimized cluster center information the Bisecting K-Means clustering has been performed, which has exhibited optimal solution for highly accurate and efficient clustering with high dimensional data sets.

Evolutionary computing based hybrid bisecting clustering algorithm for multidimensional data

Generalized tree is a more practical tree A good layout of a generalized tree is accordingly of very high application value With the definitions of a node's WPL (weighted path length) and the left or right contour in a generalized tree, an improved generalized tree layout algorithm is proposed The algorithm has both the time and space complexities of O(n) and improves the Bloesch's algorithm which has both the complexities of O(h · n) 

An improved generalized tree layout algorithm

Clustering is an important, hard and active topic in data analysis and pattern recognition. K clustering is a branch of data clustering where the number of clusters is know in advance. Recently, spectral clustering (SC) becomes one of the most popular and appealing k clustering methods because of its generality, efficiency and its rich theoretical foundation. But the final results obtained from SCs depend on spectral relaxation which may have no guarantee on the quality of the solution. In order to overcome the SCs’ shortcoming, we propose an effective GAKC algorithm by using a genetic algorithm to search for the optimal cluster result of SCs. The algorithm uses group number coding chromosome, a new uniform crossover operator and exponential mutation rate. To verify the effectiveness of GAKC, a comparison among the experimental results of the proposed GAKC, a classical GA-based method by Ujjwal Malulik and the SC methods by SM and NJW on a real-life data set is presented. The conclusion comes that the proposed algorithm can gain much more accurate clustering result.

GAKC: A New GA-Based k Clustering Algorithm

Clustering classifies data into homogeneous groups such that the objects in each group are similar and the objects between groups are not similar. Cluster algorithms can be available in many literatures. And among them, spectral clustering (SC) is one of the most popular and appealing clustering methods because of its generality, efficiency and its rich theoretical foundation. But SC algorithms need cluster number k firstly and use the top k eigenvectors of some affinity matrix as the relaxed version of the cluster result which may have no guarantee on the quality of the solution. In this paper, we explore an effective GA-based clustering algorithm for unknown k with special genetic mechanism. The feature of our proposed method is that the k can be determined as part of cluster process automatically and it can improve cluster result of SCs in term of accuracy. Experimental results illustrate the effectiveness of the proposed method.

Notice of Retraction An effective GA-based clustering algorithm for unknown k

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An improved generalized tree layout algorithm

GAKC: A New GA-Based k Clustering Algorithm

Notice of Retraction An effective GA-based clustering algorithm for unknown k