Athraa Jasim Mohammed

University of Technology, Iraq · department of computer science

Clustering is an unsupervised learning method that classified data according to similarity probabilities. DBScan as a high-quality algorithm has been introduced for clustering spatial data due to its ability to remove noise (outlier) and constructing arbitrarily shapes. However, it has a problem in determining a suitable value of Eps parameter. Thi...

Color image segmentation is widely used methods for searching of homogeneous regions to classify them into various groups. Clustering is one technique that is used for this purpose. Clustering algorithms have drawbacks such as the finding of optimum centers within a cluster and the trapping in local optima. Even though inspired meta-heuristic algor...

Nowadays, a number of artificial intelligence search algorithms have been engaged with the problem of computer networks, especially in the area of network routing problems. Nodes in a network with many connections can be called hubs and some other nodes with fewer connections can create problems in routing messages around the network. In general, t...

Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the parameters values will affect the result of forecasting model; hence, to find the optimal value of these parameters, this study investig...

The aim of robot path planning is to search for a safe path for the mobile robot. Even though there exist various path planning algorithms for mobile robots, yet only a few are optimized. The optimized algorithms include the Particle Swarm Optimization (PSO) that finds the optimal path with respect to avoiding the obstacles while ensuring safety. I...

Text clustering is one of the text mining tasks that is employed in search engines. Discovering the optimal number of clusters for a dataset or repository is a challenging problem. Various clustering algorithms have been reported in the literature but most of them rely on a pre-defined value of the k clusters. In this study, a variant of Firefly al...

Text clustering is a task of grouping similar documents into a cluster while assigning the dissimilar ones in other clusters. A well-known clustering method which is the K-means algorithm is extensively employed in many disciplines. However, there is a big challenge to determine the number of clusters using K-means. This paper presents a new cluste...

Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge. A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of clusters and the optimal centre for each cluster. I...

Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge. A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of clusters and the optimal centre for each cluster. I...

Hierarchical text clustering plays a significant role in systematically browsing, summarizing and organizing documents into structure manner. However, the Bisect K-means which is a well-known hierarchical clustering algorithm is only able to generate local optimal solutions due to the employment of K-means as part of its process. In this study, we...

The Document clustering plays significant role in Information Retrieval (IR) where it organizes documents prior to the retrieval process. To date, various clustering algorithms have been proposed and this includes the K-means and Particle Swarm Optimization. Even though these algorithms have been widely applied in many disciplines due to its simpli...

Text mining, in particular the clustering is mostly used by search engines to increase the recall and precision of a search query. The content of online websites (text, blogs, chats, news, etc.) are dynamically updated, nevertheless relevant information on the changes made are not present. Such a scenario requires a dynamic text clustering method t...

The goal of an active traffic management is to manage congestion based on current and predicted traffic conditions. This can be achieved by utilizing traffic historical data to forecast the traffic flow which later supports travellers for a better journey planning. In this study, a new method that integrates Firefly algorithm (FA) with Least Square...

Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimizati...

Document clustering is an important technique that has been widely employed in Information Retrieval (IR). Various clustering techniques have been reported, but the effectiveness of most techniques relies on the initial value of k clusters. Such an approach may not be suitable as we may not have prior knowledge on the collection of documents. To da...

Existing clustering techniques have many drawbacks and this includes being trapped in a local optima. In this paper, we introduce the utilization of a new meta-heuristics algorithm, namely the Firefly algorithm (FA) to increase solution diversity. FA is a nature-inspired algorithm that is used in many optimization problems. The FA is realized in do...

The divisive clustering has the advantage to build a hierarchical structure that is more efficient to represent documents in search engines. Its operation employs one of the partition clustering algorithms that leads to being trapped in a local optima. This paper proposes a Firefly algorithm that is based on Newton’s law of universal gravitation, k...

This paper studies two clustering algorithms that are based on the Firefly Algorithm (FA) which is a recent swarm intelligence approach. We perform experiments utilizing the Newton’s Universal Gravitation Inspired Firefly Algorithm (GFA) and Weight-Based Firefly Algorithm (WFA) on the 20_newsgroups dataset. The analysis is undertaken on two paramet...