Spectral clustering has been one of the widely used methods for community detection in networks. However, large-scale networks bring computational challenge to it. In this paper, we study spectral using randomized sketching algorithms from a statistical perspective, where typically assume network data are generated stochastic block model. To do this, first use recent developed derive two algori...

Clustering methods are machine-learning algorithms that can be used to easily select the most representative samples within a huge program trace. k-means is a popular clustering method for sampling. While k-means performs well, it has several shortcomings: (1) it depends on a random initialization, so that clustering results may vary across runs; (2) the maximal number of clusters is a user-sel...

Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Conse...

Pattern recognition is the science of data structure and its classification. There are many classification and clustering methods prevalent in pattern recognition area. In this research, rainfall data in a region in Northern Iran are classified with natural breaks classification method and with a revised fuzzy c-means (FCM) algorithm as a clustering approach. To compare these two methods, the r...

Error logs are a fruitful source of information both for diagnosis as well as for proactive fault handling – however elaborate data preparation is necessary to filter out valuable pieces of information. In addition to the usage of well-known techniques, we propose three algorithms: (a) assignment of error IDs to error messages based on Levenshtein’s edit distance, (b) a clustering approach to g...

Underwater network simulation and performance analysis require accurate packet error models. The packet error probability depends on the packet length and the temporal distribution of bit errors. We analyze error traces from the SPACE’08 experiment and show that clustering of errors occurs at several time-scales. We propose a two-part statistical error model consisting of a generalized Pareto f...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

This paper is concerned with transductive learning. Although transduction appears to be an easier task than induction, there have not been many provably useful algorithms and bounds for transduction. We present explicit error bounds for transduction and derive a general technique for devising bounds within this setting. The technique is applied to derive error bounds for compression schemes suc...

We extend our n-gram-based data-driven prediction approach from the Helping Our Own (HOO) 2011 Shared Task (Boyd and Meurers, 2011) to identify determiner and preposition errors in non-native English essays from the Cambridge Learner Corpus FCE Dataset (Yannakoudakis et al., 2011) as part of the HOO 2012 Shared Task. Our system focuses on three error categories: missing determiner, incorrect de...

This paper proposes a new mathematical model to describe the distribution of the occurrence of errors in data transmission on telephone lines. We suggest: a) that the statistics of telephone errors can be described in terms of an error probability depending solely on the time elapsed since the last occurrence of an error; b) that the distribution of inter-error intervals can be well approximate...