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Simplex growing algorithm (SGA) is an endmember finding algorithm which searches for endmembers one after another by growing simplexes one vertex at a time via maximizing simplex volume (SV). Unfortunately, several issues arise in calculating SV. One is the use of dimensionality reduction (DR) because the dimensionality of a simplex is usually much smaller than data dimensionality. Second, calculating...
For the shortcoming of the traditional focused crawler, this paper proposed an improved focused crawl method which based on syntactic dependency analysis. This method generates a words collection of the text through TF-IDF algorithm and generates a phrases collection through syntactic dependency analysis firstly. Then evaluate the collection of words and phrases to select set of keywords of the text...
In order to improve the execution efficiency of Vibe algorithm in the process of background model updating, a time and space integrated method is proposed, which can not only take the advantage of previous memoryless updating strategy, but also improve the execution efficiency of the algorithm by combining both with-memory and memoryless methods. To remove the ghost in the initialization phase of...
Infrequent itemsets are very important because many valued information can be mined from them. 2L-XMMMS model, which has been mentioned in previous studies, can mine infrequent and frequent itemsets by assigning two different minimum supports to every item. But it is based on Apriori algorithm and its efficiency is not high. To improve its efficiency, we use the FP-Growth algorithm instead of Apriori...
A group of directional sensors coordinate to form directional sensor network. Directional sensors have a limited angle of sensing and hence coverage is an important issue in directional sensor network. The sensors need to be deployed optimally to ensure maximum object coverage in the given area. In this paper, we propose a directional sensor deployment scheme. The sensors are placed based on the location...
We present elegant machine learning algorithms to efficiently learn natural language semantics (MLANLP), thus enabling much better Natural Language Computing (NLC) and Cognitive Computing (CC). Our algorithms use human brain-like learning approach and achieve very good generalization on natural language (mainly text) data. Existing machine learning algorithms performs well on numerical data and cannot...
This paper proposes an approach of recommending micro-learning path based on improved ant colony optimization algorithm. Micro-learning is a new learning style, which can be used to support learning in short time because of its micro-learning units. Each micro-learning unit consists of a small knowledge unit that can be learned at fragmented time. Meanwhile, micro-learning is more flexible than other...
The vast and ever-increasing text posting in "social networks" such as Facebook and Twitter, during the last 15 years, has produced an immense and rich text repository for several areas of knowledge. Therefore, text mining has recently become a very active and attractive area of research in computer science. The limited current understanding of the knowledge represented in these repositories...
Sequential patterns mining from data is a well stated data mining problem. It has a number of applications such as DNA sequencing, signal processing, speech analysis etc. In this problem, it is require to mine the causal relationship between different events. An event is a non-empty disordered collection of items. One of the important applications of sequential pattern mining is in medical data. Sequential...
In this article we investigate the effect of discretization Methods on the Performance of Associative Classifiers. Most of the classification approaches work on the dicretized databases. There are various approaches exploited for the discretizion of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance...
In this paper we consider the problem of training a Support Vector Machine (SVM) online using a stream of data in random order. We provide a fast online training algorithm for general SVM on very large datasets. Based on the geometric interpretation of SVM known as the polytope distance, our algorithm uses a gradient descent procedure to solve the problem. With high probability our algorithm outputs...
Modularity is widely-used objective function to detect communities and there are lots of algorithms based on modularity maximization. The leading eigenvector method is one of them where modularity is maximized by choosing the first eigenvector as partition result. To analyze in depth the information provided by other eigenvectors, modularity maximization could be transformed to vector partitioning...
During recent decades, cloud-based shared storage has become a popular storage solution. Nevertheless, current cloud-based I/O subsystem assumes that the storage devices are homogeneous. Recently, the multi-tiered storage system extends the storage hierarchy by using SSDs to cache data from the hard disks has received significant attention. Thus, in this paper, we design an I/O framework for multi-tiered...
Erasable-itemset mining is to find the itemsets (components) that can be eliminated if the products generated from them gain profit under a given threshold. In this paper, we consider the erasable-itemset integration problem to merge the erasable itemsets from two data sources. We propose an efficient merging algorithm, which can get the merged erasable itemsets directly or by rescanning partial data...
In this paper, we study the influence of using variable grouping inside mutation operators for large-scale multi-objective optimization. We introduce three new mutation operators based on the well-known Polynomial Mutation. The variable grouping in these operators is performed using two different grouping mechanisms, including Differential Grouping from the literature. In our experiments, two popular...
Elites have been widely used in many evolutionary algorithms. However, only elites in current generation are utilized to guide the learning/updating of particles/individuals in existing algorithms. Usually, elites in different generations are different and elites in the past generations may contain experienced knowledge and thus may be helpful for guiding particles/individuals to promising areas....
We present an algorithm for value based redundancy detection in Static Single Assignment(SSA) code. For this, we adopted the idea of Global Value Numbering from the Simple Algorithm for GVN (Saleena and Paleri, 2014). The novel approach is to make use of the φ-functions present at the join points to compute the meet operation. The algorithm is implemented using the LLVM compiler infrastructure. Experimental...
A phylogenetic tree is used to present the evolutionary relationships among the interesting biological species based on the similarities in their genetic sequences. The UPGMA is one of the popular algorithms to construct a phylogenetic tree according to the distance matrix created by the pairwise distances among taxa. To solve the performance issue of the UPGMA, the implementation of the UPGMA method...
In this paper, a self-adaptive two phase approach for large scaleoptimization is proposed. In the first phase, we design a uniformdiscrete search method which can quickly and roughly scan the searchspace and find good initial points. Thenwe continuously narrow the search space and make moreprecise search in a dynamically self-adaptive way. Inthe second phase, we design a dynamically self-adaptivegrouping...
In this extremely competitive era, it has become imperative for organizations to keep themselves informed about the technological advancements and their implications in their domain. It is important that they take advantage of the vast amount of knowledge out there on the web for business and strategy planning. This paper proposes methodologies for analyzing patenting and publishing data jointly to...
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