Text categorization is a significant tool to manage and organize the surging text data. Many text categorization algorithms have been explored in previous literatures, such as KNN, Naive Bayes and Support Vector Machine. KNN text categorization is an effective but less efficient classification method. In this paper, we propose an improved KNN algorithm for text categorization, which builds the classification model by combining constrained one pass clustering algorithm and KNN text categorization. Empirical results on three benchmark corpora show that our algorithm can reduce the text similarity computation substantially and outperform the-state-of-the-art KNN, Naive Bayes and Support Vector Machine classifiers. In addition, the classification model constructed by the proposed algorithm can be updated incrementally, and it has great scalability in many real-word applications.

An improved K-nearest-neighbor algorithm for text categorization

The traditional KNN text classification algorithm used all training samples for classification, so it had a huge number of training samples and a high degree of calculation complexity, and it also didn’t reflect the different importance of different samples. In allusion to the problems mentioned above, an improved KNN text classification algorithm based on clustering center is proposed in this paper. Firstly, the given training sets are compressed and the samples near by the border are deleted, so the multipeak effect of the training sample sets is eliminated. Secondly, the training sample sets of each category are clustered by k-means clustering algorithm, and all cluster centers are taken as the new training samples. Thirdly, a weight value is introduced, which indicates the importance of each training sample according to the number of samples in the cluster that contains this cluster center. Finally, the modified samples are used to accomplish KNN text classification. The simulation results show that the algorithm proposed in this paper can not only effectively reduce the actual number of training samples and lower the calculation complexity, but also improve the accuracy of KNN text classification algorithm.

/pdf/an-improved-knn-text-classification-algorithm-based-on-kndhqe0eff.pdf

An Improved KNN Text Classification Algorithm Based on Clustering

k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different data sets. The traditional KNN text classification algorithm has three limitations: (i) calculation complexity due to the usage of all the training samples for classification, (ii) the performance is solely dependent on the training set, and (iii) there is no weight difference between samples. To overcome these limitations, an improved version of KNN is proposed in this paper. Genetic Algorithm (GA) is combined with KNN to improve its classification performance. Instead of considering all the training samples and taking k-neighbors, the GA is employed to take k-neighbors straightaway and then calculate the distance to classify the test samples. Before classification, initially the reduced feature set is received from a novel method based on Rough set theory hybrid with Bee Colony Optimization (BCO) as we have discussed in our earlier work. The performance is compared with the traditional KNN, CART and SVM classifiers.

/pdf/an-improved-k-nearest-neighbor-classification-using-genetic-2npofb8mg4.pdf

An Improved k-Nearest Neighbor Classification Using Genetic Algorithm

Nowadays the exponential growth of generation of textual documents and the emergent need to structure them increase the attention to the automated classification of documents into predefined categories. There is wide range of supervised learning algorithms that deal with text classification. This paper deals with an approach for building a machine learning system in R that uses K-Nearest Neighbors (KNN) method for the classification of textual documents. The experimental part of the research was done on collected textual documents from two sources: http://egov.kz and http://www.government.kz. The experiment was devoted to challenging thing of the KNN algorithm that to find the proper value of k which represents the number of neighbors.

Using KNN algorithm for classification of textual documents

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information, popularly known as the digital/electronic information is in the form of documents, conference material, publications, journals, editorials, web pages, e-mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. In this paper we have studied the existing work in the area of text classification which will allow us to have a fair evaluation of the progress made in this field till date. We have investigated the papers to the best of our knowledge and have tried to summarize all existing information in a comprehensive and succinct manner. The studies have been summarized in a tabular form according to the publication year considering numerous key

/pdf/techniques-for-text-classification-literature-review-and-4sg5yfcujf.pdf

Techniques for text classification: Literature review and current trends

The KNN algorithm applied to text categorization is a simple, valid and non-parameter method. The traditional KNN has a fatal defect that the time of similarity computing is huge. The practicality will be lost when the KNN algorithm is applied to text categorization with the high dimension and huge samples. In this paper, a method called TFKNN(Tree-Fast-K-Nearest-Neighbor) is presented, which can search the exact k nearest neighbors quickly. In the method, a SSR tree for searching K nearest neighbors is created, in which all child nodes of each non-leaf node are ranked according to the distances between their central points and the central point of their parent. Then the searching scope is reduced based on the tree. Subsequently , the time of similarity computing is decreased largely.

A Fast KNN Algorithm for Text Categorization

In board games, game-logs record past game processes, which can be regarded as an accumulation of experience. Similar to a real person, a computer player can gradually increase its skill by learning from game-logs. Therefore, the game becomes more interesting. This paper proposes an extensible approach to mine experiential patterns from increasing game-logs. The computer player improves its strategies by utilizing these growing patterns, just as it acquires experience. To evaluate the effect and performance of the approach, we designed a sample board game as a test platform and elaborated an experiment consisting of a series of tests. Experimental results show that our approach is effective and efficient.

/pdf/mining-experiential-patterns-from-game-logs-of-board-game-3xzd62bj43.pdf

Mining experiential patterns from game-logs of board game

This paper presents a new method for the mining the hottest topics on Chinese webpage which is based on the improved k-means partitioning algorithm. The dictionary applied to word segmentation is reduced by deleting words is which are useless for clustering, and the dictionary tree is created to be applied to word segmentation. Then the speed of word segmentation is improved. Correspondence between words and integers is created by coding words. Then the title is expressed by integer set, and the cost of space and time for clustering is decreased largely. Determining the value of k is a shortcoming of stream data mining based on k-means. By this new method, the value of k is adjusted in clustering. Then both the accuracy and the speed are improved.

Mining the hottest topics on Chinese webpage based on the improved k-means partitioning

Chinese document which has relatively complicated layout can't produce the satisfied result with the traditional top-down and bottom-up layout analysis methods. This article proposes using the method based on texture to analysis them. The texture feature of document image is free of the complexity of the layout, so we can deal with the complicated layout as Chinese newspaper's using this method.

Chinese document layout analysis based on texture features

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Papers

A Fast KNN Algorithm for Text Categorization

Mining experiential patterns from game-logs of board game

Mining the hottest topics on Chinese webpage based on the improved k-means partitioning

Chinese document layout analysis based on texture features