A Novel Kernelized Fuzzy Clustering Algorithm for Data Classification

Data are expanding day by day, clustering plays a main role in handling the data and to discover knowledge from it. Most of the clustering approaches deal with the linear separable problems. To deal with the nonlinear separable problems, we introduce the concept of kernel function in fuzzy clustering. In Kernelized fuzzy clustering approach the kernel function defines the non- linear transformation that projects the data from the original space where the data are can be more separable. The proposed approach uses kernel methods to project data from the original space to a high dimensional feature space where data can be separable linearly. We performed the test on the real world datasets which shows that our proposed kernel based clustering method gives better accuracy as compared to the fuzzy clustering method.

Kant’s Original Space and Time as Mere Grounds for Possibilities

In the Critique of Pure Reason Kant appears to make incompatible claims regarding the unitary natures of what he takes to be our a priori representations of space and time. I argue that these representations are unitary independently of all synthesis and explain how this avoids problems encountered by other positions regarding the Transcendental Deduction and its relation to the Transcendental Aesthetic in that work. Central is the claim that these representations (1) contain, when characterized as intuitions and considered as prior to any affections of sensibility, only an infinitude of merely possible finite spatial and temporal representations, and (2) are representations that are merely transcendental grounds for the possibilities for receiving or generating finite representations in sensibility that are determined (immediately, in the case of reception) by means of syntheses that accord with the categories.

Improvement of Support Vector Machine Algorithm in Big Data Background

With the rapid development of the Internet and the rapid development of big data analysis technology, data mining has played a positive role in promoting industry and academia. Classification is an important problem in data mining. This paper explores the background and theory of support vector machines (SVM) in data mining classification algorithms and analyzes and summarizes the research status of various improved methods of SVM. According to the scale and characteristics of the data, different solution spaces are selected, and the solution of the dual problem is transformed into the classification surface of the original space to improve the algorithm speed. Research Process. Incorporating fuzzy membership into multicore learning, it is found that the time complexity of the original problem is determined by the dimension, and the time complexity of the dual problem is determined by the quantity, and the dimension and quantity constitute the scale of the data, so it can be based on the scale of the data Features Choose different solution spaces. The algorithm speed can be improved by transforming the solution of the dual problem into the classification surface of the original space. Conclusion. By improving the calculation rate of traditional machine learning algorithms, it is concluded that the accuracy of the fitting prediction between the predicted data and the actual value is as high as 98%, which can make the traditional machine learning algorithm meet the requirements of the big data era. It can be widely used in the context of big data.

Popular branchings and their dual certificates

Let G be a digraph where every node has preferences over its incoming edges. The preferences of a node extend naturally to preferences over branchings, i.e., directed forests; a branching B is popular if B does not lose a head-to-head election (where nodes cast votes) against any branching. Such popular branchings have a natural application in liquid democracy. The popular branching problem is to decide if G admits a popular branching or not. We give a characterization of popular branchings in terms of dual certificates and use this characterization to design an efficient combinatorial algorithm for the popular branching problem. When preferences are weak rankings, we use our characterization to formulate the popular branching polytope in the original space and also show that our algorithm can be modified to compute a branching with least unpopularity margin. When preferences are strict rankings, we show that “approximately popular” branchings always exist.

Efficient Image Inpainting Using Dimensional Space Reduction via Adaptive Patch-based Concept and Rank Lowering Technique

In this paper, we present a new algorithm for image inpainting using low dimensional feature space. In our method, projecting a low dimensional space from the original space is accomplished firstly using SVD, which is named low rank component, and then the missing pixels are filled in the new space. Finally, the original image is inpainted so that adaptive patch size is considered by quad-tree based on the previous step. In our algorithm, the missing pixels in the target region are estimated twice, one in low dimension feature space and another in the original space. It is noticeable that both processes estimate the unknown pixels using patch-based idea and rank lowering concept. Experimental results of this algorithm show better consistency in comparison with state-of-the-art methods.

A glance into the anatomy of monotonic maps

<p>Given an autohomeomorphism on an ordered topological space or its subspace, we show that it is sometimes possible to introduce a new topology-compatible order on that space so that the same map is monotonic with respect to the new ordering. We note that the existence of such a re-ordering for a given map is equivalent to the map being conjugate (topologically equivalent) to a monotonic map on some homeomorphic ordered space. We observe that the latter cannot always be chosen to be order-isomorphic to the original space. Also, we identify other routes that may lead to similar affirmative statements for other classes of spaces and maps.</p>

Semi-supervised optimization algorithm basedon Laplacian Eigenmaps

As a member of many dimensionalityreduction algorithms, manifold learning is the hotspot ofrecent dimensionality reduction algorithm. Despite it isgood at retaining the original space structure, there is nodenying that its effect of classifying still has room forimprovement. Based on Laplacian Eigenmap, which is oneof the manifold learning algorithm, this paper committed tooptimize the algorithm combined with a semi-supervisedlearning ideas, which can improve the recognition rate.Finally, the better method of two forms is tested in thesurface electromyography system and plant leafidentification system. The experimental results show thatthis semi-supervised method does well in classifying.

Using Agent-based Simulation for Safety: Fact-finding about a crowd accident to improve public space design

With the growing city density and large gatherings happening all over the world, crowd safety has become a new topic. This research discusses how to diagnosis and improve crowd safety in urban public space by analysing a real crowd accident that happened in Shanghai in 2014 using an agent-based simulator. Fact-finding analysis shows that insufficient capacity of the whole area, density difference in bottleneck stairs and lack of separation measurements in front of bottleneck stairs are the main causes of the accident. According to the media query towards the original space plan, we made two alternative plans in the bottleneck area and tested their performances.