In this paper, a data mining approach is proposed for duration prediction of the town trips (travel time) in New York City. In this regard, at first, two novel approaches, including a mathematical and a statistical approach, are proposed for grouping categorical variables with a huge number of levels. The proposed approaches work based on the cost matrix generated by repetitive post-hoc tests f...

Simulating natural phenomena at greater accuracy results in an explosive growth of data. Largescale simulations with particles currently involve ensembles consisting of between 10-10 particles, which cover 10-10 timesteps. Thus, the data files produced in a single run can reach from tens of gigabytes to hundreds of terabytes. This data bank allows one to reconstruct the spatio-temporal evolutio...

This paper studies a new form of nearest neighbor queries in spatial databases, namely, mutual nearest neighbor (MNN) search. Given a set D of objects and a query object q, an MNN query returns from D, the set of objects that are among the k 1 (P1) nearest neighbors (NNs) of q; meanwhile, have q as one of their k 2 (P1) NNs. Although MNN queries are useful in many applications involving decisio...

A sandwich-structured flexible supercapacitor electrode has been developed based on MnO2 nanonest (MNN) modified ionic liquid (IL) functionalized graphene paper (GP), which is fabricated by functionalizing graphene nanosheets with an amine-terminated IL (i.e., 1-(3-aminopropyl)-3-methylimidazolium bromide) to form freestanding IL functionalized GP (IL-GP), and then modifying IL-GP with a unique...

In the last decades, many efforts have been made to solve multimodal optimization problems using Particle Swarm Optimization (PSO). To produce good results, these PSO algorithms need to specify some niching parameters to define the local neighborhood. In this paper, our motivation is to propose the novel neighborhood structures that remove undesirable niching parameters without sacrificing perf...

Spectral manifold learning techniques have recently found extensive applications in machine vision. The common strategy of spectral algorithms for manifold learning is exploiting the local relationships in a symmetric adjacency graph, which is typically constructed using -nearest neighborhood ( -NN) criterion. In this paper, with our focus on locally linear embedding as a powerful and well-know...

We study the mixing time of a Markov chain Mnn on permutations that performs nearest neighbor transpositions in the non-uniform setting, a problem arising in the context of self-organizing lists. We are given “positively biased” probabilities {pi,j ≥ 1/2} for all i < j and let pj,i = 1− pi,j . In each step, the chain Mnn chooses two adjacent elements k, and ` and exchanges their positions with ...

Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions We propose a locally adaptive form of nearest neighbor classification to try to finesse this curse of dimensionality. We use a local linear discriminant analysis to estimate an effective metric for computing neighborhoods. We determine the local decision b...

Artificial neural networks have proven to be an attractive mathematical tool to represent complex relationships in many branches of hydrology. Due to this attractive feature, neural networks are increasingly being applied in subsurface modeling where intricate physical processes and lack of detailed field data prevail. In this paper, a methodology using modular neural networks (MNN) is proposed...

Mental nerve neuropathy (MNN) or "numb chin syndrome" is a rare neurologic symptom most often associated with malignancy. Patients typically develop paresthesia or numbness localized to the chin and lower lip and will often seek care at their local emergency department. Pain and expansion of the lower jaw may also be present. We report a case of MNN associated with a metastatic lesion in the ma...