Fuzzy clustering methods identify naturally occurring clusters in a dataset, where the extent to which different are overlapped can differ. Most have parameter fix level of fuzziness. However, appropriate fuzziness depends on application at hand. This paper presents an entropy c-means (ECM), method fuzzy that simultaneously optimizes two contradictory objective functions, resulting creation with levels allows ECM degrees overlap. functions using multiobjective optimization methods,...

Simultaneous Coalition Structure Generation and Assignment (SCSGA) is an important research problem in multi-agent systems. Given n agents m tasks, the aim of SCSGA to form disjoint coalitions such that between tasks there a one-to-one mapping, which ensures each coalition capable accomplishing assigned task. with Multi-dimensional Features (SCSGA-MF) extends by introducing d-dimensional vector for agent We propose heuristic algorithm called Multiple Distance Metric (MDM) approach solve...

Alzheimer's disease (AD), characterized by progressive cognitive decline and memory loss, presents a formidable global health challenge, underscoring the critical importance of early precise diagnosis for timely interventions enhanced patient outcomes. While MRI scans provide valuable insights into brain structures, traditional analysis methods often struggle to discern intricate 3D patterns crucial AD identification. Addressing this we introduce an alternative end-to-end deep learning...

This paper presents a common algorithm for the kernel k-harmonic means (KKHM) and fuzzy c-means (KFCM) clustering problems. We incorporate functions in generalized c- cost function, forming function of kernelized general (KGFCM) problem, design an to locally minimize this function. The KGFCM has two parameters: exponent p Euclidean distance, weighting m. By setting proper values m our algorithm, one can execute KKHM or KFCM algorithm. Using KKHM, we compare its performance with popular...

Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to need careful tuning before constructing Laplacian. In this study, we present a novel pruning algorithm create sparse called \emph{spectral on p-neighborhood retained matrix} (SC-pNA). Our method improves node-specific fixed neighbor selection by allowing variable number of neighbors, eliminating external data as...

VIKNET (visual knowledge network), an extension of associative networks that is specifically designed for representation recognition 3-D objects, introduced. The design criteria, largely influenced by early vision processing, are discussed. formally described as algebraic structure, with network functions defined on it. input image, also in the form a network, recognized partial matching algorithm. A completely worked out example demonstrates efficacy scheme even shapes subjected to viewing...

The White Matter Hyperintensities (WMH) clearly visible on T2 weighted FLAIR brain MR images are associated with a number of neurodegenerative diseases including Alzheimer's Disease, vascular dementia, stroke, late-onset late-life depression, Multiple Sclerosis etc. Automated Segmentation WMH from the MRI is used as diagnostic tool in neuro medicine. In this work we have developed algorithm to segment using Fuzzy C-means method multiple stages. steps involved preprocessing image, skull...

Visual semantic networks, a representation scheme for library of visual object models, are introduced. New models learned in the with help knowledge engineer, who informs system generic class each new example, and then discovers potential cases further classification, gets them confirmed by engineer. The details discovery discussed, it is argued that behavior more or less independent order presentation examples. Experiments small number mechanical tools confirm this.< <ETX...

Multiple kernel clustering methods have been quite successful recently especially concerning the multi-view of complex datasets. These simultaneously learn a multiple metric while in an unsupervised setting. With motivation that some minimal supervision can potentially increase their effectiveness, we propose Kernel Transfer Clustering (MKTC) method be described terms two tasks: source task, where is learned, and target task transferred to partition dataset. In create weakly supervised...