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...

In this paper, we devise an efficient algorithm for clustering market-basket data items. In view of the nature of clustering market basket data, we devise in this paper a novel measurement, called the small-large (abbreviated as SL) ratio, and utilize this ratio to perform the clustering. With this SL ratio measurement, we develop an efficient clustering algorithm for data items to minimize the...

Although it has a relatively short history, conceptual clustering is an especially active area of research in machine learning. There are a variety of ways in which conceptual patterns (the Al contribution to clustering) play a role in the clustering process. Two distinct conceptual clustering paradigms (conceptual sorting of exemplars and concept discovery) are described briefly. Then six type...

Multi-view subspace clustering (MVSC) can effectively group multi-view data distributed around several low-dimensional subspaces. Although encouraging results, most existing methods suffer from two typical limitations, resulting in performance degradation. They ignore high-order correlations underlying the data, leading to degeneration of complementary power; addition, they rely on much prior k...

Waroenk Akul is a variety of food places located in the Pasar Minggu area ​​South Jakarta, where still applying method ordering directly to seller by means customer getting menu book and recording desired order using books pens provided, based on application. android here this aims make it easier for customers choose their respective customer's cellphones, feature also available that often orde...

Abstract Graph learning is being increasingly applied to image clustering reveal intra-class and inter-class relationships in data. However, existing graph learning-based focuses on grouping images under a single view, which under-utilises the information provided by To address that, we propose self-supervised multi-view technique contrastive heterogeneous learning. Our method computes affinity...

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...

In this work clustering and recognition problem of fonts in document images is addressed. Various font features and their clustering behavior are investigated. Font clustering is implemented both from shape similarity or from OCR performance points of view. A font recognition algorithm is developed that can identify the font group or the individual font from which a text was created. © 2001 SPI...

A framework for multi-agent based clustering is described whereby individual agents represent individual clusters. A particular feature of the framework is that, after an initial cluster configuration has been generated, the agents are able to negotiate with a view to improving on this initial clustering. The framework can be used in the context of a number of clustering paradigms, two are inve...