### A Comparative Analysis of Particle Swarm Optimization and K-means Algorithm For Text Clustering Using Nepali Wordnet

"... ABSTRACT The volume of digitized text documents on the web have been increasing rapidly. As there is huge collection of data on the web there is a need for grouping(clustering) the documents into clusters for speedy information retrieval. Clustering of documents is collection of documents into grou ..."

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ABSTRACT The volume of digitized text documents on the web have been increasing rapidly. As there is huge collection of data on the web there is a need for grouping(clustering) the documents into clusters for speedy information retrieval. Clustering of documents is collection of documents into groups such that the documents within each group are similar to each other and not to documents of other groups. Quality of clustering result depends greatly on the representation of text and the clustering algorithm. This paper presents a comparative analysis of three algorithms namely K-means, Particle swarm Optimization (PSO) and hybrid PSO+K-means algorithm for clustering of text documents using WordNet. The common way of representing a text document is bag of terms. The bag of terms representation is often unsatisfactory as it does not exploit the semantics. In this paper, texts are represented in terms of synsets corresponding to a word. Bag of terms data representation of text is thus enriched with synonyms from WordNet. K-means, Particle Swarm Optimization (PSO) and hybrid PSO+K-means algorithms are applied for clustering of text in Nepali language. Experimental evaluation is performed by using intra cluster similarity and inter cluster similarity. .

### CLUSTERING OF DATA SETS BY USING FUZZY ALGORITHM

"... ABSTRACT In this Technological era Clustering is inevitable. For any function arrangement of Data is a primary task. The collected Data has to be grouped based on their features, Clustering is a method of arranging same or similar attributes and that attributes which are closer to each other are al ..."

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ABSTRACT In this Technological era Clustering is inevitable. For any function arrangement of Data is a primary task. The collected Data has to be grouped based on their features, Clustering is a method of arranging same or similar attributes and that attributes which are closer to each other are also grouped together. Clustering is formed of three major process initializing Data is the principle process, Data sets are selected randomly and distance metrics are used. Iteration reduction is a great challenge as for clustering is concerned. Fuzzy c-means is applied with the intention of reducing iteration. This Fuzzy c-means permits one data to function in two sets. When iteration is reduced clustering will be more effective. This paper deals with intervention of Fuzzy c-means algorithm in a specified Data set which thereby is to reduce iteration to make the function flaw less and reliable.

### Dynamically Adaptive Data Clustering Using Intelligent Swarm-like Agents

"... Abstract ⎯ Inspired by the self-organized behaviour of bird flocks, a new dynamic clustering approach based on Particle Swarm Optimization is proposed. This paper introduces a novel clustering method, the PSDC, a new Particle Swarm-like agents approach for Dynamically Adaptive data clustering. Unli ..."

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Abstract ⎯ Inspired by the self-organized behaviour of bird flocks, a new dynamic clustering approach based on Particle Swarm Optimization is proposed. This paper introduces a novel clustering method, the PSDC, a new Particle Swarm-like agents approach for Dynamically Adaptive data clustering. Unlike other partition clustering algorithms, this technique does not require initial partitioned seeds and it can dynamically adapt to the changes in the global shape or size of the clusters. In this technique, the agents have lots of useful features such as sensing, thinking, making decisions, parallelism and moving freely in the solution space. The moving swarm-like agents are guided to move according to a specific proposed navigation rules. These rules help every agent to find its new position in its navigation process and the clustering results emerge from the collective and cooperative behaviour of these swarm agents. If the swarm performance showed gradual improvements during a predefined number of cycles, then the current population could pass useful information to the next population in order to help further generations in reaching better solutions faster and enable the learning process to be reinforced. The distributed, adaptive and cooperative behaviour of these agents was so powerful to explore the solution space effectively. Through the cooperative behaviour, the generations of agents were able to build knowledge and the whole population could pass information to the next generation. Numerous experiments have been conducted using both synthetic and real datasets to evaluate the efficiency of the proposed model. Cluster validity approaches are used to quantitatively evaluate the results of the clustering algorithm. Experimental results showed that the proposed particle swarm-like clustering algorithm reaches good clustering solutions and achieves superior performance compared to others.

### A Particle Swarm Optimization based fuzzy c means approach for efficient web document clustering

"... Abstract-There is a need to organize a large set of documents into categories through clustering so as to facilitate searching and finding the relevant information on the web with large number of documents becomes easier and quicker. Hence we need more efficient clustering algorithms for organizing ..."

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Abstract-There is a need to organize a large set of documents into categories through clustering so as to facilitate searching and finding the relevant information on the web with large number of documents becomes easier and quicker. Hence we need more efficient clustering algorithms for organizing documents. Clustering on large text dataset can be effectively done using partitional clustering algorithms. The Fuzzy C-means algorithm is the most suitable partitional clustering approach for handling large dataset with respect to execution time. This paper introduces a new Hybrid Particle Swarm Optimization method that combines the best features of PSO and fuzzy C-means algorithms for efficient web document clustering. We have tested this hybrid PSO algorithm on various text document collections. The document range varies from 512 to 1639 in the dataset and the terms ranges from 12367 to 19851. Based on the experimental results our proposed PSOFCM approach performs better clustering than other method.

### Hierarchical Data Clustering Model for Analyzing Passengers Trip in Highways

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### Sabnis, Ameya, "Hybrid Clustering with Application to Web Pages " (2009). Master's Projects. Paper 74.

, 2009

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### A New Clustering Approach based on Glowworm Swarm Optimization

"... Abstract-High-quality clustering techniques are required for the effective analysis of the growing data. Clustering is a common data mining technique used to analyze homogeneous data instance groups based on their specifications. The clustering based nature-inspired optimization algorithms have rec ..."

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Abstract-High-quality clustering techniques are required for the effective analysis of the growing data. Clustering is a common data mining technique used to analyze homogeneous data instance groups based on their specifications. The clustering based nature-inspired optimization algorithms have received much attention as they have the ability to find better solutions for clustering analysis problems. Glowworm Swarm Optimization (GSO) is a recent nature-inspired optimization algorithm that simulates the behavior of the lighting worms. GSO algorithm is useful for a simultaneous search of multiple solutions, having different or equal objective function values. In this paper, a clustering based GSO is proposed (CGSO), where the GSO is adjusted to solve the data clustering problem to locate multiple optimal centroids based on the multimodal search capability of the GSO. The CGSO process ensures that the similarity between the cluster members is maximized and the similarity among members from different clusters is minimized. Furthermore, three special fitness functions are proposed to evaluate the goodness of the GSO individuals in achieving high quality clusters. The proposed algorithm is tested by artificial and real-world data sets. The better performance of our proposed algorithm over four popular clustering algorithms is demonstrated on most data sets. The results reveal that CGSO can efficiently be used for data clustering.

### Projected Clustering Particle Swarm Optimization and Classification

"... Abstract. Supervised learning algorithms are trained with labeled data only. But labeling the data can be costly and hence the amount of labeled data available may be limited. Training the classifiers with limited amount of labeled data can lead to low classification accuracy. Hence pre-processing ..."

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Abstract. Supervised learning algorithms are trained with labeled data only. But labeling the data can be costly and hence the amount of labeled data available may be limited. Training the classifiers with limited amount of labeled data can lead to low classification accuracy. Hence pre-processing the data is required for getting better classification accuracy. Full dimensional clustering has been used in literature as preprocessing step to classification methods. But in high dimensional data different clusters may exist in different subspaces of the dataset. Projected Clustering Particle Swarm Optimization (PCPSO) finds optimal centers of subspace clusters by optimizing a subspace cluster validation index. In this paper we use PCPSO method to find subspace clusters present in the dataset. The subspace clusters found and limited amount of available labeled data are used to label the large amount of unlabelled data that is present in the dataset. Various classification methods are then applied on the data pre-processed by using PCPSO. In this paper we propose PCPSO-Classification method. Various new classification methods like PCPSO-Naive bayes, PCPSO-Multi layer perceptron and PCPSO-Decision table can be obtained by using different classification methods like Naive bayes, Multi layer perceptron and Decision table respectively in the classification stage of proposed PCPSO-Classification method. When the dataset contains subspace clusters and labeling the data is costly due to which available labeled data is limited then the structure of data may be used along with available limited labeled data to label the large amount of unlabeled data. After pre-processing the data the amount of labeled data is not limited. We applied PCPSO-Naive bayes, PCPSO-Multi layer perceptron and PCPSO-Decision table methods on synthetic datasets and found classification accuracy improved significantly compared to using Naive bayes, Multi layer perceptron and Decision table for classification with limited available labeled data for training classifiers. The subspace clusters found by PCPSO can be used for different types of pre-processing for solving different problems before applying classification methods on datasets. In this paper we considered the problem of limited labeled data and using PCPSO to find subspace clusters which are used for labeling large amount of unlabeled data with the help of available limited labeled data.

### Under the Guidance of

"... This is to certify that the thesis entitled, “Classification Of Synthetic Aperture Radar ..."

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This is to certify that the thesis entitled, “Classification Of Synthetic Aperture Radar

### 2 Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization

"... Abstract. This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of h ..."

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Abstract. This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrates the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.