In this paper a new identification method for non-linear hybrid systems that have mixed continuous and discrete states by using fuzzy clustering and principal component analysis is described. The method first determines the hybrid characteristic of the system inspired by an inverse form of the merge method for clusters, which makes it possible to identify the unknown switching points of a proce...

This paper introduces a minimalistic approach to produce a visual hybrid map of a mobile robot’s working environment. The proposed system uses omnidirectional images along with odometry information to build an initial dense posegraph map. Then a two level hybrid map is extracted from the dense graph. The hybrid map consists of global and local levels. The global level contains a sparse topologi...

In data mining, C-Means clustering is well known for its efficiency proved good for large data sets. The aim of every clustering algorithm is to group the similar data items while ungroup the dissimilar items. C-Means clustering algorithm has the opposite principle as fuzzy clustering algorithm has i.e. in C-Means every point has belonging to clusters while in fuzzy clustering, they belong to o...

dna microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. the average value of the fluorescent intensity could be calculated in a microarray experiment. the calculated intensity values are very close in amount to the levels of expression of a particular gene. however, determining the appropriate position of every spot in microarray im...

This paper presents our approach to enhance the portability of acoustic models by mitigating the phonetic mismatch arising from a new testing task which is rather different from the training data. The approach is a hybrid one which combines knowledge-based context categorization to generate a context rich set of subword units, and data-driven-based acoustic model clustering on the level of cont...

Application Layer Distributed Denial of Service (App-DDoS) attack has become a major threat to web security. Attack detection is difficult as they mimic genuine user request. This paper proposes a clustering based correlation approach for detecting application layer DDoS attack on HTTP protocol. Proposed approach has two main modules ----Flow monitoring module and User behavior monitoring modul...

Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast an...

Clustering is the most common form of unsupervised learning.In clustering, it is the distribution and makeup of the data that will determine cluster membership. It needs representation of objects and similarity measure. which compares distribution of features between objects. For the high dimensionality, feature extraction and feature selection improves the performance of clustering algorithms....

In this paper, we propose a hybrid clustering method that combines the strengths of bottom-up hierarchical clustering with that of top-down clustering. The first method is good at identifying small clusters but not large ones; the strengths are reversed for the second method. The hybrid method is built on the new idea of a mutual cluster: a group of points closer to each other than to any other...

Cluster analysis is a useful technique in multivariate statistical analysis. Different types of hierarchical cluster analysis and K-means have been used for data analysis in previous studies. However, the K-means algorithm can be improved using some metaheuristics algorithms. In this study, we propose simulated annealing based algorithm for K-means in the clustering analysis which we refer it a...