Anikó Vágner

The GridOPTICS clustering algorithm

The OPTICS algorithm is a hierarchical density-based clustering method. It creates reachability plots to identify all clusters in the point set. Nevertheless, it has limitation, namely it is very slow for large data sets. We introduce the GridOPTICS algorithm, which builds a grid structure to reduce the number of data points, then it applies the OPTICS clustering algorithm on the grid structure. In order to get the clusters, the algorithm uses the reachability plots of the grid structure, then it determines to which cluster the original input points belong. The experimental results show that our new algorithm is faster than the OPTICS, the speed-up can be one or two orders of magnitude or more, which depends mainly on the τ parameter of the GridOPTICS algorithm. At the end of the article, we give some advice to which point set you can apply the GridOPTICS algorithm. Keyword: clustering, large data set, OPTICS, grid

Public transit schedule and route planner application for mobile devices

In this paper we introduce a public transit schedule and route planner application for urban public transportation. The application was developed for the Android Operating System. It works offline and does not need a permanent internet connection. While transport agencies usually make their schedules available online, browsing them outdoors is not always possible, or requires too much effort on the small screen of a mobile phone. The solutions of this problem are the GTFS databases, which describe the urban public transportation systems. Developers can create applications which use the GTFS databases. However, these databases are not applicable for fast processing needed for route planning on mobile devices with limited resources. Therefore we created a data structure from the GTFS database which is easier to manage, smaller and faster to process. The Android application uses this data structure. The application lists the departure times, journey times and the stops of the lines. It can display the lines on the map. It also shows the stops nearby with the name of the lines passing through. The user can choose one or more stops and view details about the lines passing through. The main function of the application is the route planner. It plans at least one route from a starting-point to a destination. Before the route planning the user can set parameters which are taken into account by the route planner.

Clustering and Visualization of ECG Signals

Holter electrocardiographic (ECG) recordings are ambulatory long-term registers that are used to detect heart diseases. These recordings normally include more than one channel and their durations are up to 24 hours. The principal problem of the cardiologists is the manual inspection of the whole Holter ECG in order to find all those beats which morphologically differ from the normal beats. In this paper we present our method. Firstly, we apply a grid clustering technique. Secondly, we use a special density-based clustering algorithm, named Optics. Then we visualize every heart beat in the record, heartbeats in a cluster, furthermore we represent every cluster with median of heartbeats. We can perform manual. With this method the ECG is easily analyzed and the time of processing is optimized.

Visualization and Off-line Processing of Blood Pressure Signals

In the public health care it is very common that microcontroller calculates the result of oscillometric blood pressure measurements. In this case the result can be imprecise; it does not inform the patient and the doctor accordingly. The recordings collected by the microcontroller can be sent to an application which runs on a PC. The recording can contain only one measurement or sequence of measurements created during 24 hours. The advantage of the PC side application is that it can use more memory and processor capacity, so it is faster and more precise. The task of the application is to calculate and visualize the values of blood pressure. The application determines the values of the blood pressure based on an oscillometric blood pressure algorithm. The application visualizes the result of each step of the algorithm. The algorithm decides whether the result is acceptable and authentic based on the characteristic of the recording. The other part of the application helps in the validation. It executes the algorithm on mass of the recordings which have result of reference measurement. The application shows the differences between the results of the algorithm and the values of reference. The application helps to qualify the algorithm according to the international standards. The application works well under laboratory circumstances. But application needs further validation so that it can be put to the market.