The analysis of moving entities “trajectories” is an important task in different application domains, since it enables the analyst to design, evaluate and optimize navigation spaces. Trajectory clustering is aimed at identifying the objects moving in similar paths and it helps the analysis and obtaining of efficient patterns. Since clustering depends mainly on similarity, the computing similari...

Clustering is a classical data analysis technique that is applied to a wide range of applications in the sciences and engineering. For very large data sets, the performance of a clustering algorithm becomes critical. Although clustering has been thoroughly studied over the last decades, little has been done on utilizing modern multi-processor machines to accelerate the analysis process. We prop...

Multi-view spectral clustering has drawn much attention due to the effectiveness of exploiting similarity relationships among data points. These methods typically reveal intrinsic structure using a predefined graph for each view. The graphs are fused consensus one, on which final results obtained. However, such common strategies may lead information loss because inconsistency or noise multiple ...

In this paper, a reduced dimensionality representation is learned from multiple views of the processed data. These multiple views can be obtained, for example, when the same underlying process is observed using several different modalities, or measured with different instrumentation. The goal is to effectively utilize the availability of such multiple views for various purposes such as non-line...

Developing data mining techniques both suitable for databases as well as to maintain the individual privacy becomes the primary aim of research based on privacy preserving data mining (PPDM). The present PDDM clustering methods perform privacy preservation only with single point of view in which every tuple in the data matrix for data holder containing single sensitive attribute is been conside...

Artificial intelligence has been provided powerful research attributes like data mining and clustering for reducing bigdata functioning. Clustering in multi-labeled categorical analysis gives huge amount of relevant that explains evaluation portrayal qualities as trending notion. A wide range scenarios, from many dimensions may be used to provide efficient results. Multi-view techniques had out...

In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete clustering, view-missing increases difficulty learning common representations from different To address challenge, propose a novel framework, which incorporates cross-view relation transfer and fusion learning. Specifically, based consistency existing in data, devise transfer-based comple...

The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. However, the local learning methods developed so far are all devised for single-view problems. In fact, in many real-world applications, examples are represented by multiple sets of features. In this paper, we extend t...

This paper presents a novel system for multi-pitch tracking, i.e. estimate the pitch trajectory of each monophonic source in a mixture of harmonic sounds. The system consists of two stages: multi-pitch estimation and pitch trajectory formation. In the first stage, we propose a new approach based on modeling spectral peaks and non-peak regions to estimate pitches and polyphony in each single fra...