Calculate missing value using association rules mining

Discovering hidden knowledge from hug amount of data in form of association rules mining havebecome very popular in scaling field of data mining. One several algorithms have been alsodeveloped for mining association rules. All those algorithms can be effectively applied on all asdataset where data has not any time granularity means non-temporal dataset. The quality of trainingdata for knowledge discovery in databases (KDD) and data mining depends upon so many factors,but also handling missing values is considered to be a crucial factor in whole data quality. Today inreal world datasets contains missing values due to human, in operational error, hardwaremalfunctioning and many other factors

Scope analysis of different kinds of fingerprint

As Biometrics are the most widely used technique for person identification and verificationwith different applications, this research paper is mainly focused on the different kinds ofFingerprints based on availability and acquisition process. Fingerprints can broadly becategorized into three categories: Live Scan, Latent, and Patent Fingerprints. The mainobjective of this research is to present importance of different kinds of fingerprint with theirrecognition techniques. The quality of fingerprint image also plays an important role duringthe recognition process, because it requires extra time to improve quality of image. Thereare different types of image enhancement methods are available applicable differently ondifferent kinds of fingerprint images. This research provides basic information aboutfingerprints to the research direction

Survey on Service Based Ratings of Users by Exploring Geographical Location

Recommendation systems help online users with advantageous access to the items and services they may be intrested on this present reality. Because of the requirements of compelling forecast and productive recommendation, it is advantageous for the location-based services (LBS), to discover the user's next location that the user may visit. So in this paper, diverse kinds of methodologies used to discover, anticipate, and examine location based services are talked about. It is important to convey those expectation and recommendation services for ongoing real time application with direction mapping. While considering location information's, at that point the information measure ended up noticeably colossal and dynamic. Finding ideal answer for anticipate the rating in view of the location and unequivocal conduct is overviewed

Метод прогнозування місцезнаходження рухомих об’єктів

Growing popularity of location based services is leading to an increasing volume of mobility data. Inthis paper we introduce a data mining approach to the problem of predicting the next location of amoving object with a certain level of accuracy. We use apriori algorithm to build a probabilisticmodel of future object location. The experiments have demonstrated that our technique givesreasonably accurate predictionРост уровня распространения сервисов, ориентированных на позиционирование движущихсяобъектов, приводит к быстрому росту объемов мобильных данных. В данной статье авторами предложен подход к решению проблемы прогнозирования последующего местонахождения движущегося объекта с определенным уровнем точности. При этом используется априорныйалгоритм для построения вероятностной модели последующего местонахождения объекта.Эксперименты показали, что предложенный подход обеспечивает приемлемый уровень точности предсказанияЗростання рівня розповсюдження сервісів, орієнтованих на позиціонування рухомих об’єктів,призводить до швидкого зростання обсягів мобільних даних. В даній статті авторами запропоновано підхід до розв’язання проблеми прогнозування наступного місцезнаходження рухомого об’єкта з визначеним рівнем точності. При цьому використовується апріорний алгоритмдля побудови імовірнісної моделі майбутнього місцезнаходження об’єкта. Експерименти продемонстрували, що запропонований підхід забезпечує прийнятний рівень точності передбаченн

Semantic-Based Destination Suggestion in Intelligent Tourism Information Systems

Abstract. In recent years, there has been a growing interest in mining trajectories of moving objects. Advances in this data mining task are likely to support the development of new applications such as mobility prediction and service pre-fetching. Approaches reported in the literature consider only spatio-temporal information provided by collected trajectories. However, some applications demand additional sources of information to make correct predictions. In this work, we consider the case of an on-line tourist support service which aims at suggesting places to visit in the nearby. We assume tourist interests depend both on her/his geographical position and on the “semantic ” information extracted from geo-referenced documents associated to the visited sites. Therefore, the suggestion is based on both spatio-temporal data as well as on textual data. To deal with tourist’s interest drift we apply a time-slice density estimation method. Experimental results are reported for two scenarios.

Predicting Future Location of a Moving Object

Tato práce se věnuje návrhu a implementaci aplikace pro predikci budoucí lokace pohybujícího se objektu. Popisuje metodu predikce založenou na algoritmu WhereNext. Tento algoritmus získá z databáze trajektorií objektů T-Patterny, které představují frekventované vzory pohybu objektů, a ty následně použije k predikci. Algoritmus byl implementovaný v programovacím jazyku Java a jeho funkčnost je odzkoušena na vygenerované datové sadě pohybu aut.This thesis deals with the design and the implementation of the application for predicting future location of a moving object. It describes a method for prediction based on the algorithm WhereNext. This algorithm obtains T-Patterns from a database of trajectories of objects, which represent frequent patterns of movement of objects, and those subsequently uses for prediction. The algorithm was implemented in programming language Java and its functionality was tested on a generated dataset of movement of cars.

Modeling cell migration in quantitative image analysis

Tese de mestrado em Tecnologias da Informação aplicadas às Ciências Biológicas e Médicas, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012All biological phenomena are dynamic and movement is an essential function in cellular systems but their regulation, characteristics and physiological meaning are not fully known. Measurement of the cell movements provides quantitative information that is inevitable for understanding the cellular system. Cell migration is a field of intense current research generating high amounts of image data that need to be quantitatively analyzed with efficiency, consistency and completeness. To accomplish, computerized motion analysis is rapidly becoming a requisite. Since all the existing algorithms for these purposes are often not robust, effective and optimal enough to yield satisfactory results, new and alternative methods must be developed. The aim of this work is to find and develop an alternative to the tracking of individual cells in order to, visualize, characterize and quantify the migration characteristics of cell population. This alternative comprises the implementation of a simple and automated algorithm to obtain qualitative and quantitative information from image sequences of cell migration in a fast, easy and inexpensive computationally way. After an extensive literature review, it became clear that all the methodologies and approaches employed to make the quantitative analysis of cell migration only presented solutions that involved object tracking. And the new method developed estimates the probability density functions for cell migration and was implemented as a plugin (Migration) for ImageJ, as cross platform open source application. In the evaluation of the developed algorithm was taken in to account his applicability, efficiency, consistency, completeness and validity. It can be used to in image sequences to extract information regarding the distribution of the future positions of all particles in a determined time point in the future and is quick when is executing. The results obtained with this method were satisfactory. Comparing to existing approaches to study the cell migration this method adds an improvement, it can deal with complex situation, such as overlapping of particles or other occlusions.Todos os fenómenos biológicos são dinâmicos e o movimento é uma função essencial nos sistemas celulares, mas a sua regulação, características e significado fisiológico não são totalmente conhecidos. A medição dos movimentos das células providencia informação quantitativa para compreender o sistema celular. A migração de células é um campo de intensa investigação gerando grandes quantidades de dados que necessitam de ser quantitativamente analisados com eficiência, consistência e de maneira completa. Para tal, a análise do movimento através dos sistemas de informação está a tornar-se cada vez mais num requisito. Dado que os algoritmos disponíveis para este propósito não são muitas vezes robustos, eficientes e óptimos para proporcionarem resultados satisfatórios, métodos alternativos devem ser desenvolvidos e implementados. O objectivo deste trabalho é encontrar e desenvolver uma alternativa para o tracking de células de modo a se visualizar, caracterizar e quantificar a migração de células. Esta alternativa requer a implementação de um algoritmo simples e automático para obter a informação, quer qualitativa, quer quantitativa de um vídeo, com imagens da migração de células, de um modo rápido e fácil. Depois de uma revisão bibliográfica extensa, verificou-se que todos os métodos implementados para fazer a análise quantitativa da migração de células eram soluções de tracking de partículas. O novo método aqui desenvolvido estima as funções de densidade de probabilidade para a migração de células e foi implementado como um plugin (Migration) para o ImageJ. A avaliação do algoritmo desenvolvido teve em conta a sua aplicabilidade, eficiência, consistência e validade. Pode ser usado em vídeos e extrair informação relativa à estimação da distribuição das posições de todas as partículas num determinado momento no tempo, executando de maneira rápida. Todos os resultados obtidos com este novo método são satisfatórios. Comparando com as abordagens conhecidas da literatura, este método apresenta uma melhoria, pode lidar com situações complexas, tais como sobreposição de partículas e outras oclusões