12688706
Unsupervised Learning from Complex Data: The Matrix Incision Tree Algorithm
Analysis of large-scale gene expression data requires novel methods for knowledge discovery and predictive model building as well as clustering. Organizing data into meaningful structures is one of the most fundamental modes of learning. DNA microarray data set can be viewed as a set of mutually associated genes in a high-dimensional space. This paper describes a novel method to organize a complex high-dimensional space into successive lower-dimensional spaces based on the geometric properties of the data structure in the absence of a priori knowledge. The matrix incision tree algorithm reveals the hierarchical structural organization of observed data by determining the successive hyperplanes that 'optimally' separate the data hyperspace. The algorithm was tested against published data sets yielding promising results.
5105796
Two-stage normalization using background intensities in cDNA microarray data
BackgroundIn the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.ResultsIn this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods.ConclusionsFor the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image analysis, the proposed two-stage normalization method can be applicable as long as both signal intensity and background intensity are available.
731257
A Novel Framework for Discovering Robust Cluster Results
We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process.
6065142
On effective data clustering in bitemporal databases
Temporal databases provide built-in supports for efficient recording and querying of time-evolving data. In this paper, data clustering issues in temporal database environment are addressed. Data clustering is one of the most effective techniques that can improve performance of a database system. However, data clustering methods for conventional databases do not perform well in temporal databases because there exist crucial differences between their query patterns. We propose a data clustering measure, called Temporal Affinity, that can be used for the clustering of temporal data. The temporal affinity, which is based on the analysis of query patterns in temporal databases, reflects the closeness of temporal data objects in viewpoints of temporal query processing. We perform experiments to evaluate the proposed measure. The experimental results show that a data clustering method with the temporal affinity works better than other methods.
3103350
Random Matchings in Regular Graphs
d-regular graph G, let M be chosen uniformly at random from the set of all matchings of G, and for let be the probability that M does not cover x.We show that for large d, the 's and the mean μ and variance of are determined to within small tolerances just by d and (in the case of μ and ) :
Theorem. For any d-regular graph G,
(a), so that ,
(b),where the rates of convergence depend only on d.
1231793
An effective data clustering measure for temporal selection and projection queries
Abstract Temporal databases (TDBs) allow users to record and retrieve time-varying data objects. Since TDBs usually manage a huge amount of underlying data objects, efficient disk accesses are essential for fast response time in temporal query processing. Data clustering is one of the most effective techniques that can improve performance of TDB systems. However, clustering measures for conventional data objects are not appropriate to temporal data objects because it is important to exploit temporal properties of underlying data objects and temporal queries as data clustering criteria. In this paper, we propose a data clustering measure called temporal affinity that can be used for effective temporal data clustering. The temporal affinity, which is based on the semantics of temporal operators, reflects the closeness among temporal data objects with respect to temporal query processing. We perform experiments to show the effectiveness of the proposed temporal data clustering measure. The experimental results indicate that a data clustering method based on the temporal affinity works better than other methods.
9445026
Efficient Uplink Scheduler Architecture of Subscriber Station in IEEE 802.16 System
The IEEE 802.16, broadband fixed wireless access standard, defines four service classes, USG, rtPS, nrtPS and BE on high speed wireless networks. To guarantee the QoS requirement of these classes, the subscriber station and base station require scheduling architecture and algorithm. However, the IEEE 802.16 does not define any scheduling architecture or algorithm, and the most existing scheduling mechanisms only focus on working at the BS. In this paper, we propose two types of scheduling architecture working at the SS. In the one-level scheduler, we use a flow queue and class queue by differentiating flows with their class priority. The two-level scheduler can provide more organized QoS service with complementing the one-level scheduler. Adapting these architectures makes scheduler efficiently control all types of traffic defined in the IEEE 802.16. In the proposed architecture, any scheduling algorithms such as SCFQ and EDF can be applied. We evaluate the proposed scheduling architecture by simulation. The results of the simulation show that our proposed architecture can use the bandwidth efficiently.
6738575
Agent-oriented software modeling
Due to the increased applications of agents, agent-oriented software becomes large and complex. To support systematic developments of such software, the agent-oriented software development methodology needs to be developed. This paper focuses on the modeling phase of agent-oriented software development. For agent-oriented software modeling, the Agent Elicitation method, Intra and Inter Agent modeling method are proposed. Agent Elicitation shows how to extract agents from classes in the real world. Modeling methods of agent's characteristics-Goal, Belief, Plan and Capability-are proposed for Intra Agent modeling. Methods of agent's mobility and communication in multiagent systems are proposed for Inter Agent modeling.