A study was conducted to segment a digital music file, such as an MP3 file. The study demonstrated that musical segmentation was performed by using MPEG-7 features and constrained clustering based on Means. A team of researchers developed a method, called RefraiD that defects the chorus sections of music and can detect key changes in choruses, using the 12-dimensional chroma feature vector. The researchers investigated musical segmentation of structural components, using Mel frequency Cepstral Coefficient (MFCC) and compared the sequence approach of structural segmentation with the state approach (HMM). The researchers showed that the state approach is more robust and computationally efficient. A method was also proposed for musical segmentation by detecting boundaries and aggregation.
The study concentrates on fuzzy relational calculus and views it as a basis of data granulation and data compression. In this setting, data and images, in particular, are represented as fuzzy relations. We investigate fuzzy relational equations as a vehicle of data compression. It is shown that both compression and decompression (reconstruction) phases are closely linked with the way in which fuzzy relational equations are usually being formulated and solved. The underlying findings that are encountered in the theory of these equations are easily accommodated as an important backbone of any relational compression. The character of the solutions to the equations make them ideal for reconstruction purposes, as they specify the extremal elements of the solution set and in such a way help establish some envelopes of the original images under compression. The flexibility of the conceptual and algorithmic framework arising there is also discussed. Numerical examples provide a suitable illustrative material emphasizing the main features of the compression mechanisms.
Software metrics aid project managers in predicting the quality of software systems. A method is proposed using a neural network classifier with metric inputs and subjective quality assessments as class labels. The labels are adjusted using fuzzy measures of the distances from each class center computed using robust multivariate medians.
In this paper, we describe an experiment, which analyzes the relative importance and stability of change metrics for predicting defects for 3 releases of the Eclipse project. The results indicate that out of 18 change metrics 3 metrics contain most information about software defects. Moreover, those 3 metrics remain stable across 3 releases of the Eclipse project. A comparative analysis with the full model shows that the prediction accuracy is not too much affected by using a subset of 3 metrics and the recall even improves.
In this article, we are concerned with the formation of type-2 information granules in a two-stage approach. We present a comprehensive algorithmic framework which gives rise to information granules of a higher type (type-2, to be specific) such that the key structure of the local granular data, their topologies, and their diversities become fully reflected and quantified. In contrast to traditional collaborative clustering where local structures (information granules) are obtained by running algorithms on the local datasets and communicating findings across sites, we propose a way of characterizing granular data (formed) by forming a suite of higher type information granules to reveal an overall structure of a collection of locally available datasets. Information granules built at the lower level on a basis of local sources of data are weighted by the number of data they represent while the information granules formed at the higher level of hierarchy are more abstract and general, thus facilitating a formation of a hierarchical description of data realized at different levels of detail. The construction of information granules is completed by resorting to fuzzy clustering algorithms (more specifically, the well-known Fuzzy C-Means). In the formation of information granules, we follow the fundamental principle of granular computing, viz., the principle of justifiable granularity. Experimental studies concerning selected publicly available machine-learning datasets are reported.
Due to its inferior characteristics, an observed (noisy) image's direct use gives rise to poor segmentation results. Intuitively, using its noise-free image can favorably impact image segmentation. Hence, the accurate estimation of the residual between observed and noise-free images is an important task. To do so, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual estimation and leads noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related fidelity term derived from the distribution of different types of noise. Built on this framework, we present a weighted $\ell_{2}$-norm fidelity term by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. Supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over existing FCM-related algorithms.
