An incremental FP-growth web content mining and its application in preference identification

This paper presents a real application of Web-content mining using an incremental FP-Growth approach. We firstly restructure the semi-structured data retrieved from the web pages of Chinese car market to fit into the local database, and then employ an incremental algorithm to discover the association rules for the identification of car preference. To find more general regularities, a method of attribute-oriented induction is also utilized to find customer’s consumption preferences. Experimental results show some interesting consumption preference patterns that may be beneficial for the government in making policy to encourage and guide car consumption.

Chronic intermittent hypoxia and incremental cycling exercise independently depress muscle in vitro maximal Na+-K+-ATPase activity in well-trained athletes

Athletes commonly attempt to enhance performance by training in normoxia but sleeping in hypoxia [live high and train low (LHTL)]. However, chronic hypoxia reduces muscle Na⁺-K⁺-ATPase content, whereas fatiguing contractions reduce Na⁺-K⁺-ATPase activity, which each may impair performance. We examined whether LHTL and intense exercise would decrease muscle Na⁺-K⁺-ATPase activity and whether these effects would be additive and sufficient to impair performance or plasma K⁺ regulation. Thirteen subjects were randomly assigned to two fitness-matched groups, LHTL (n = 6) or control (Con, n = 7). LHTL slept at simulated moderate altitude (3,000 m, inspired O₂ fraction = 15.48%) for 23 nights and lived and trained by day under normoxic conditions in Canberra (altitude ~600 m). Con lived, trained, and slept in normoxia. A standardized incremental exercise test was conducted before and after LHTL. A vastus lateralis muscle biopsy was taken at rest and after exercise, before and after LHTL or Con, and analyzed for maximal Na⁺-K⁺-ATPase activity [K⁺-stimulated 3-O-methylfluorescein phosphatase (3-O-MFPase)] and Na⁺-K⁺-ATPase content ([³H]ouabain binding sites). 3-O-MFPase activity was decreased by –2.9 ± 2.6% in LHTL (P < 0.05) and was depressed immediately after exercise (P < 0.05) similarly in Con and LHTL (–13.0 ± 3.2 and –11.8 ± 1.5%, respectively). Plasma K⁺ concentration during exercise was unchanged by LHTL; [3H]ouabain binding was unchanged with LHTL or exercise. Peak oxygen consumption was reduced in LHTL (P < 0.05) but not in Con, whereas exercise work was unchanged in either group. Thus LHTL had a minor effect on, and incremental exercise reduced, Na⁺-K⁺-ATPase activity. However, the small LHTL-induced depression of 3-O-MFPase activity was insufficient to adversely affect either K⁺ regulation or total work performed.

Estimating the total energy demand for supra-maximal exercise using the VO2-power regression from an incremental exercise test

The VO_2-power regression and estimated total energy demand for a 6-minute supramaximal exercise test was predicted from a continuous incremental exercise test. Sub-maximal VO_2- power co-ordinates were established from the last 40 seconds(s) of 150-second exercise stages. The precision of the estimated total energy demand was determined using the 95% confidence interval (95% CI) of the estimated total energy demand. The linearity of the individual VO_2-power regression equations was determined using Pearson's correlation coefficient. The mean 95% CI of the estimated total energy demand was 5.9±2.5 mL O₂ Eq•^-1kg•min^-1, and the mean correlation coefficient was 0.9942±0.0042. The current study contends that the sub-maximal VO_2-power co-ordinates from a continuous incremental exercise test can be used to estimate supra-maximal energy demand without compromising the precision of the accumulated oxygen deficit (AOD) method.

Finding coverage using incremental attribute combinations

Coverage is the range that covers only positive samples in attribute (or feature) space. Finding coverage is the kernel problem in induction algorithms because of the fact that coverage can be used as rules to describe positive samples. To reflect the characteristic of training samples, it is desirable that the large coverage that cover more positive samples. However, it is difficult to find large coverage, because the attribute space is usually very high dimensionality. Many heuristic methods such as ID3, AQ and CN2 have been proposed to find large coverage. A robust algorithm also has been proposed to find the largest coverage, but the complexities of time and space are costly when the dimensionality becomes high. To overcome this drawback, this paper proposes an algorithm that adopts incremental feature combinations to effectively find the largest coverage. In this algorithm, the irrelevant coverage can be pruned away at early stages because potentially large coverage can be found earlier. Experiments show that the space and time needed to find the largest coverage has been significantly reduced.

Incremental and adaptive clustering stream data over sliding window

Cluster analysis has played a key role in data stream understanding. The problem is difficult when the clustering task is considered in a sliding window model in which the requirement of outdated data elimination must be dealt with properly. We propose SWEM algorithm that is designed based on the Expectation Maximization technique to address these challenges. Equipped in SWEM is the capability to compute clusters incrementally using a small number of statistics summarized over the stream and the capability to adapt to the stream distribution’s changes. The feasibility of SWEM has been verified via a number of experiments and we show that it is superior than Clustream algorithm, for both synthetic and real datasets.

Incremental institutional change and Australian universities

Much has been written and researched about transformational change and the exogenous events that result in radical institutional transformation. This paper examines institutions as building blocks of social order comprising power and politics and shared understanding to bring about change. Thelen and Mahoney (2010) go beyond a general model of change that describes the collapse of one set of institutional norms to be replaced by another. The model of change proposed takes into account both exogenous as well as endogenous factors as being the source of institutional change. They go on to state that a view of transformation change as being a result of abrupt, wholesale breakdown needs to be rethought to include incremental, endogenous shifts in thinking that can often result in fundamental transformations. This paper gives consideration to these issues to propose the Australian Higher Education sector as a unique sample in which to investigate this type of change.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

A multi-agent incremental negotiation scheme for meetings scheduling

We present an agent-oriented approach to the meeting scheduling problem and propose an incremental negotiation scheme that makes use of a hierarchical structure of an individual agent's working knowledge. First, we formalise the meeting scheduling problem in a multi-agent context, then elaborate on the design of a common agent architecture of all agents in the system. As a result, each agent becomes a modularised computing unit yet possesses high autonomy and robust interface with other agents. The system reserves the meeting participants' privacy since there are no agents with dominant roles, and agents can communicate at an abstract level in their hierarchical structures

Face recognition via incremental 2DPCA

Recently, the Two-Dimensional Principal Component Analysis (2DPCA) model is proposed and proved to be an efficient approach for face recognition. In this paper, we will investigate the incremental 2DPCA and develop a new constructive method for incrementally adding observation to the existing eigen-space model. An explicit formula for incremental learning is derived. In order to illustrate the effectiveness of the proposed approach, we performed some typical experiments and show that we can only keep the eigen-space of previous images and discard the raw images in the face recognition process. Furthermore, this proposed incremental approach is faster when compared to the batch method (2DPCD) and the recognition rate and reconstruction accuracy are as good as those obtained by the batch method.