K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from a series of disadvantages; it is only able to find local minima and the positions of the initial clustering centres (centroids) can greatly affect the clustering solution. Over the years many K-Means variations and initialisation techniques have been proposed with different degrees of complexity. In this study we focus on common K-Means variations along with a range of deterministic and stochastic initialisation techniques. We show that, on average, more sophisticated initialisation techniques alleviate the need for complex clustering methods. Furthermore, deterministic methods perform better than stochastic methods. However, there is a trade-off: less sophisticated stochastic methods, executed multiple times, can result in better clustering. Factoring in execution time, deterministic methods can be competitive and result in a good clustering solution. These conclusions are obtained through extensive benchmarking using a range of synthetic model generators and real-world data sets.

/pdf/an-empirical-comparison-between-stochastic-and-deterministic-gm4xpf6igw.pdf

An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations

The internet contains a large amount of data, to exploit it a special treatment of its content should be done, and a particular web information system should be developed. As a critical step of those systems, the web page classification is proposed. In this paper, we present the characteristics of web page classification, we reviewed the different machine learning algorithms used to categorize web pages. Finally, we track some assumptions from the studied methods.

A Review of Machine Learning Algorithms for Web Page Classification

K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from a series of disadvantages, such due to only being able to find local minima, the positions of the initial clustering centres (centroids) can greatly affect the clustering solution. Over the years many K-Means variations and initialisation techniques have been proposed with different degrees of complexity. In this study we focus on common K-Means variations and deterministic initialisation techniques and we first show that more sophisticated initialisation methods reduce or alleviates the need of complex K-Means clustering, and secondly, that deterministic methods can on average achieve better performance than stochastic methods. However, there is a trade-off: stochastic methods executed multiple times can result to better clustering. Nevertheless, factoring in execution time deterministic methods can be competitive and result in a good clustering solution. These conclusions are obtained through extensive benchmarking using different data set model generators from various studies as well as standalone clustering and real-world data sets.

An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations

K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency. Its properties and limitations have been investigated by many works reported in the literature. K-means, though, suffers from computational problems when dealing with large datasets with many dimensions and great number of clusters. Therefore, many authors have proposed and experimented different techniques for the parallel execution of K-means. This paper describes a novel approach to parallel K-means which, today, is based on commodity multicore machines with shared memory. Two reference implementations in Java are developed and their performances are compared. The first one is structured according to a map/reduce schema that leverages the built-in multi-threaded concurrency automatically provided by Java to parallel streams. The second one, allocated on the available cores, exploits the parallel programming model of the Theatre actor system, which is control-based, totally lock-free, and purposely relies on threads as coarse-grain “programming-in-the-large” units. The experimental results confirm that some good execution performance can be achieved through the implicit and intuitive use of Java concurrency in parallel streams. However, better execution performance can be guaranteed by the modular Theatre implementation which proves more adequate for an exploitation of the computational resources.

/pdf/performance-of-parallel-k-means-algorithms-in-java-1htbsibb.pdf

Performance of Parallel K-Means Algorithms in Java

Gambling and porn websites are more and more harmful to the health and growth of the youth with the rapid development of the Internet, however, the text contents and URLs based website classification methods could not get satisfying on gambling and porn websites detection because domain names of them change fast. Meanwhile, the visual based website classification has gotten perfect results in phishing website detection which encourages us. Therefore, we introduce the visual feature to identify gambling websites and porn websites in this paper. Firstly, we develop a website screenshot tool which could save the full contents of a website to be a image, Secondly, the effective feature is chosen by BoW model to recognize the screenshots of gambling websites and porn websites, and the appropriate parameters are chosen to promote the efficiency of classification. Finally, experimental results on our collected gambling websites and porn website datasets demonstrate that our proposed method is able to recognize the gambling and porn websites and gets satisfying results.

Identifying Gambling and Porn Websites with Image Recognition

K-means is one of the most significant clustering algorithms in data mining. It performs well in many cases, especially in the massive data sets. However, the result of clustering by K-means largely depends upon the initial centers, which makes K-means difficult to reach global optimum. In this paper, we developed a novel algorithm based on finding density peaks to optimize the initial centers for K-means. In the experiment, together with our algorithm, nine different clustering algorithms were extensively compared on four well-known test data sets. According to our experimental results, the performance of our algorithm is significantly better than other eight algorithms, which indicates that it is a valuable method to select initial center for K-means.

Density K-means: A new algorithm for centers initialization for K-means

Nowadays, while we are enjoying the convenience brought by such a huge repository of online web information, we may come across difficulties in finding the web pages we want related to particular information we are searching for. Hence, it is essential to classify web documents to facilitate the search and retrieval of pages. Existing algorithms work well with a small quantity of web pages, whereas, they become slow and even non-effective while dealing with a large scale of web pages. Recently, some of these algorithms were adapted to distributed platforms which boosted their classification speeds effectively. However, due to high dimensions of web page features, the parallel classifiers were still trained with limited capacity training sets. In addition, these methods didn't improve the classification itself, merely boosted by high computing performance of distributed platforms. So oriented to large-scale web page classification, we propose to integrate anchor graph hashing with K-Nearest Neighbour(KNN) classifier to reduce the pages' original feature dimensions. The hash value of each page is used for training and classification instead of the original vectors. Experimental comparison with the original KNN on a large dataset demonstrates the efficacy of our proposed method.

LWCS: A large-scale web page classification system based on anchor graph hashing

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Density K-means: A new algorithm for centers initialization for K-means

LWCS: A large-scale web page classification system based on anchor graph hashing