Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. Recently, a new problem of optimizing processing of sets of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Mine Merge and Common Counting have been proposed and ...

The progressive edge-growth (PEG) algorithm is known to construct low-density parity-check (LDPC) codes at finite code lengths with large girths by establishing edges between symbol and check nodes in an edge-by-edge manner. The linear-encoding PEG (LPEG) algorithm, a simple variation of the PEG algorithm, can be applied to generate linear time encodable LDPC codes whose m parity bits p1, p2, ....

Recent experimental studies elucidating the importance of noise in gene regulation have ignited widespread interest in Gillespie's stochastic simulation technique for biochemical networks. We formulate modifications to the Gillespie algorithm which are necessary to correctly simulate chemical reactions with time-dependent reaction rates. We concentrate on time dependence of kinetic rates arisin...

The linear least mean squares (LMS) algorithm has been recently extended to a reproducing kernel Hilbert space, resulting in an adaptive filter built from a weighted sum of kernel functions evaluated at each incoming data sample. With time, the size of the filter as well as the computation and memory requirements increase. In this paper, we shall propose a new efficient methodology for constrai...

Frequent embedded subtree pattern mining is an important data mining problem with broad applications. In this paper, we propose a novel embedded subtree mining algorithm, called PrefixTreeESpan (i.e. Prefix-Treeprojected Embedded-Subtree pattern), which finds a subtree pattern by growing a frequent prefix-tree. Thus, using divide and conquer, mining local length-1 frequent subtree patterns in P...

The emergence of cloud computing solves the problems that traditional data mining algorithms encounter when dealing with large data. This paper studies the FP-Growth algorithm and proposes a parallel linked list-based FPG algorithm based on MapReduce programming model, named as the PLFPG algorithm. And then it describes the main idea of algorithm. Finally, by using different data sets to test t...

A fast algorithm is presented for computing the growth distance between a pair of convex objects in three-dimensional space. The growth distance is a measure of both separation and penetration between objects. When the objects are polytopes represented by their faces, the growth distance is determined by the solution of a Linear Program (LP). This article presents a new approach to the solution...

Achieving robot autonomy is a fundamental objective in Mobile Robotics. However in order to realise this goal, a robot must be aware of its location within an environment. Therefore, the localisation problem (i.e.,the problem of determining robot pose relative to a map of its environment) must be addressed. This paper proposes a new biology-inspired approach to this problem. It takes advantage ...

Data mining has been widely recognized as a powerful tool to explore added value from large-scale databases. Finding frequent item sets in databases is a crucial in data mining process of extracting association rules. Many algorithms were developed to find the frequent item sets. This paper presents a summary and a comparative study of the available FP-growth algorithm variations produced for m...

Discovery of frequent itemsets is a very important data mining problem with numerous applications. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on frequent itemset mining has been done so far, focusing mainly on developing faster complete mining al...