18562452
Interactive constrained frequent-pattern mining system
Data mining refers to the search for implicit, previously unknown, and potentially useful information (such as frequent patterns) that might be embedded in data. Most of the existing data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. Moreover, data mining is supposed to be an exploratory process. In this context, we are working on a project with the objective of implementing an efficient, interactive, human-centered system for mining frequent patterns that satisfy the user constraints. We develop such a system, called iCFP, for interactive mining of constrained frequent patterns. Our developed system uses a tree-based mining framework. In addition, it (i) allows human users to impose a certain focus on the mining process, (ii) provides users with feedback during the mining process, and (iii) permits users to dynamically change their constraints during the process.
3110883
OSSM: a segmentation approach to optimize frequency counting
Computing the frequency of a pattern is one of the key operations in data mining algorithms. We describe a simple yet powerful way of speeding up any form of frequency counting satisfying the monotonicity condition. Our method, the optimized segment support map (OSSM), is a light-weight structure which partitions the collection of transactions into m segments, so as to reduce the number of candidate patterns that require frequency counting. We study the following problems: (1) what is the optimal number of segments to be used; and (2) given a user-determined m, what is the best segmentation/composition of the m segments? For Problem 1, we provide a thorough analysis and a theorem establishing the minimum value of m for which there is no accuracy lost in using the OSSM. For Problem 2, we develop various algorithms and heuristics, which efficiently generate OSSMs that are compact and effective, to help facilitate segmentation.
12213746
Embedded Voxel Colouring
The reconstruction of a complex scene from multiple im- ages is a fundamental problem in the field of computer vision. Volu- metric methods have proven to be a strong alternative to traditional correspondence-based methods due to their flexible visibility models. In this paper we analyse existing methods for volumetric reconstruction and identify three key properties of voxel colouring algorithms: a water-tight surface model, a monotonic carving order, and causality .W e present a new Voxel Colouring algorithm which embeds all reconstructions of a scene into a single output. While modelling exact visibility for arbitrary camera locations, Embedded Voxel Colouring removes the need for a priori threshold selection present in previous work. An efficient imple- mentation is given along with results demonstrating the advantages of posteriori threshold selection.
9755080
A 1.2V buck converter with a novel on-chip low-voltage current-sensing scheme
A low-voltage on-chip current-sensing circuit for buck power converter is proposed. The minimum supply voltage of the proposed current-sensing circuit is 1.2V (where V/sub th/ = 0.85V at room temperature), and the sensing accuracy is higher than 94%. The proposed buck converter is able to operate at a 1.2-V supply with higher than 98% power-conversion efficiency and with a maximum output current of 120mA.