Search results for " CLUSTER"
showing 10 items of 2162 documents
Modelling Systemic Cojumps with Hawkes Factor Models
2013
Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.
A novel heuristic memetic clustering algorithm
2013
In this paper we introduce a novel clustering algorithm based on the Memetic Algorithm meta-heuristic wherein clusters are iteratively evolved using a novel single operator employing a combination of heuristics. Several heuristics are described and employed for the three types of selections used in the operator. The algorithm was exhaustively tested on three benchmark problems and compared to a classical clustering algorithm (k-Medoids) using the same performance metrics. The results show that our clustering algorithm consistently provides better clustering solutions with less computational effort.
Twister Tries
2015
Many commonly used data-mining techniques utilized across research fields perform poorly when used for large data sets. Sequential agglomerative hierarchical non-overlapping clustering is one technique for which the algorithms’ scaling properties prohibit clustering of a large amount of items. Besides the unfavorable time complexity of O(n 2 ), these algorithms have a space complexity of O(n 2 ), which can be reduced to O(n) if the time complexity is allowed to rise to O(n 2 log2 n). In this paper, we propose the use of locality-sensitive hashing combined with a novel data structure called twister tries to provide an approximate clustering for average linkage. Our approach requires only lin…
Cluster-Based RF Fingerprint Positioning Using LTE and WLAN Outdoor Signals
2015
In this paper we evaluate user-equipment (UE) positioning performance of three cluster-based RF fingerprinting methods using LTE and WLAN signals. Real-life LTE and WLAN data were collected for the evaluation purpose using consumer cellular-mobile handset utilizing ‘Nemo Handy’ drive test software tool. Test results of cluster-based methods were compared to the conventional grid-based RF fingerprinting. The cluster-based methods do not require grid-cell layout and training signature formation as compared to the gridbased method. They utilize LTE cell-ID searching technique to reduce the search space for clustering operation. Thus UE position estimation is done in short time with less comput…
An efficient cluster-based outdoor user positioning using LTE and WLAN signal strengths
2015
In this paper we propose a novel cluster-based RF fingerprinting method for outdoor user-equipment (UE) positioning using both LTE and WLAN signals. It uses a simple cost effective agglomerative hierarchical clustering with Davies-Bouldin criterion to select the optimal cluster number. The positioning method does not require training signature formation prior to UE position estimation phase. It is capable of reducing the search space for clustering operation by using LTE cell-ID searching criteria. This enables the method to estimate UE positioning in short time with less computational expense. To validate the cluster-based positioning real-time field measurements were collected using readi…
Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination
2015
Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well when the number of items to be clustered is large. The best known algorithms are characterized by quadratic complexity. This is a generally accepted fact and cannot be improved without using specifics of certain metric spaces. Twister tries is an algorithm that produces a dendrogram (i.e., Outcome of a hierarchical clustering) which resembles the one produced by AHC, while only needing linear space and time. However, twister tries are sensitive to rare, but still possible, hash evaluations. These might have a disastrous effect on the final outcome. We propose the use of a metaheuristic algor…
Supporting Institutional Awareness and Academic Advising using Clustered Study Profiles
2017
The purpose of academic advising is to help students with developing educational plans that support their academic career and personal goals, and to provide information and guidance on studies. Planning and management of the students’ study path is the main joint activity in advising. Based on a study log of passed courses, we propose to use robust, prototype-based clustering to identify a set of actual study path profiles. Such profiles identify groups of students with similar progress of studies, whose analysis and interpretation can be used for better institutional awareness and to support evidence-based academic advising. A model of automated academic advising system utilizing the possi…
Scalable implementation of dependence clustering in Apache Spark
2017
This article proposes a scalable version of the Dependence Clustering algorithm which belongs to the class of spectral clustering methods. The method is implemented in Apache Spark using GraphX API primitives. Moreover, a fast approximate diffusion procedure that enables algorithms of spectral clustering type in Spark environment is introduced. In addition, the proposed algorithm is benchmarked against Spectral clustering. Results of applying the method to real-life data allow concluding that the implementation scales well, yet demonstrating good performance for densely connected graphs. peerReviewed
Optical Properties of Monolayer-Protected Aluminum Clusters: Time-Dependent Density Functional Theory Study
2015
We examine the electronic and optical properties of experimentally known monolayer-protected aluminum clusters Al4(C5H5)4, Al50(C5Me5)12, and Al69(N(SiMe3)2)183– using time-dependent density functional theory. By comparing Al4(C5H5)4 and the theoretical Al4(N(SiMe3)2)4 cluster, we observe significant changes in the optical absorption spectra caused by different hybridization between metal core and ligands. Using these initial observations, we explain the calculated spectra of Al50(C5Me5)12 and Al69(N(SiMe3)2)183–. Al50(C5Me5)12 shows a structured spectrum with clear regions of low-intensity core-to-core transitions followed by high-intensity ligand-to-core transitions due to its high symmet…
Symmetry breaking in ligand-protected gold clusters probed by nonlinear optics
2016
The first hyperpolarizabilities of [Au25(SR)18](-1/0) and Au38(SR)24 clusters were determined by Hyper-Rayleigh Scattering. A strong dependence on the molecular symmetry was observed, and we explore two strategies to destroy the center of inversion in [Au25(SR)18](-1/0), protection by chiral ligands and alloying of the cluster with silver. This may open new avenues to applications of Au : SR clusters in second-order nonlinear optics.