Search results for "Clusterin"
showing 10 items of 478 documents
An evolutionary restricted neighborhood search clustering approach for PPI networks
2014
Protein-protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of a group of proteins strictly related can be useful to predict protein functions. Clustering techniques have been widely employed to detect significant biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions intr…
Sectors on sectors (SonS): A new hierarchical clustering visualization tool
2011
Clustering techniques have been widely applied to extract information from high-dimensional data structures in the last few years. Graphs are especially relevant for clustering, but many graphs associated with hierarchical clustering do not give any information about the values of the centroids' attributes and the relationships among them. In this paper, we propose a new visualization approach for hierarchical cluster analysis in which the above-mentioned information is available. The method is based on pie charts. The pie charts are divided into several pie segments or sectors corresponding to each cluster. The radius of each pie segment is proportional to the number of patterns included i…
Clustering categorical data: A stability analysis framework
2011
Clustering to identify inherent structure is an important first step in data exploration. The k-means algorithm is a popular choice, but K-means is not generally appropriate for categorical data. A specific extension of k-means for categorical data is the k-modes algorithm. Both of these partition clustering methods are sensitive to the initialization of prototypes, which creates the difficulty of selecting the best solution for a given problem. In addition, selecting the number of clusters can be an issue. Further, the k-modes method is especially prone to instability when presented with ‘noisy’ data, since the calculation of the mode lacks the smoothing effect inherent in the calculation …
A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data …
2013
Abstract Background Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function; (2) choice of a clustering algorithm; (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Results A procedure is proposed for the assessment of the discriminative ability of a distance functi…
A Microcalcification Detection System in Mammograms based on ANN Clustering
2018
Breast cancer is one of the leading causes to women mortality in the world. Clustered microcalcifications (MCs) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this work, we present a novel method for the detection of MCs in mammograms which consists of regions of Interest (ROIs) segmentation, based on a spatial filter that allows the detection of small and large microcalcifications, clustering and classification of MCs by Artificial Neural Network. The system has been tested on a public dataset of digital images and compared with previous approaches. The results demonstrate that the proposed approach could achie…
Percolation on correlated random networks
2011
We consider a class of random, weighted networks, obtained through a redefinition of patterns in an Hopfield-like model and, by performing percolation processes, we get information about topology and resilience properties of the networks themselves. Given the weighted nature of the graphs, different kinds of bond percolation can be studied: stochastic (deleting links randomly) and deterministic (deleting links based on rank weights), each mimicking a different physical process. The evolution of the network is accordingly different, as evidenced by the behavior of the largest component size and of the distribution of cluster sizes. In particular, we can derive that weak ties are crucial in o…
Plastic or not plastic? That’s the problem: analysing the Italian students purchasing behavior of mineral water bottles made with eco-friendly packag…
2022
Abstract European Strategy for Plastics in a Circular Economy draws new shapes of economy in order to protect the environment and reduce marine pollution, GHGs and countries’ dependence on imported fossil fuels. The core of EU Strategy is also to try to transform the way plastic products are designed, produced, used and recycled in the EU. Italy is the first country in Europe and the second in the world for consumption of bottled water, with remarkable environmental impacts, from production to distribution. Starting from social science theory, this work aims to investigate consumers' behavior and the related factors that influence their behavior pertaining to the purchase of. mineral water …
Classification based on Iterative Object Symmetry Transform
2004
The paper shows an application of a new operator named the iterated object transform (IOT) for cell classification. The IOT has the ability to grasp the internal structure of a digital object and this feature can be usefully applied to discriminate structured images. This is the case of cells representing chondrocytes in bone tissue, giarda protozoan, and myeloid leukaemia. A tree classifier allows us to discriminate the three classes with a good accuracy.
A New Platform for Automatic Bottom-Up Electric Load Aggregation
2017
In this paper, a new virtual framework for load aggregation in the context of the liberalized energy market is proposed. Since aggregation is managed automatically through a dedicated platform, the purchase of energy can be carried out without intermediation as it happens in peer-to-peer energy transaction models. Differently from what was done before, in this new framework, individual customers can join a load aggregation program through the proposed aggregation platform. Through the platform, their features are evaluated and they are clustered according to their reliability and to the width of range of regulation allowed. The simulations show the deployment of an effective clustering and …
A PCA-based clustering algorithm for the identification of stratiform and convective precipitation at the event scale: an application to the sub-hour…
2021
AbstractUnderstanding the structure of precipitation and its separation into stratiform and convective components is still today one of the important and interesting challenges for the scientific community. Despite this interest and the advances made in this field, the classification of rainfall into convective and stratiform components is still today not trivial. This study applies a novel criterion based on a clustering approach to analyze a high temporal resolution precipitation dataset collected for the period 2002–2018 over the Sicily (Italy). Starting from the rainfall events obtained from this dataset, the developed methodology makes it possible to classify the rainfall events into f…