A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms. Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases. Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing, text mining, XML, and parallel and distributed processing.     

Distributed Mining of Maximal Frequent Itemsets on a Data Grid System

In this paper, we propose a new algorithm, named Grid-based Distributed Max-Miner (GridDMM), for mining maximal frequent itemsets from databases on a Data Grid. A frequent itemset is maximal if none of its supersets is frequent. GridDMM is specifically suitable for use in Grid environments due to low communication and synchronization overhead. GridDMM consists of a local mining phase and a global mining phase. During the local mining phase, each node mines the local database to discover the local maximal frequent itemsets, then they form a set of maximal candidate itemsets for the top-down search in the subsequent global mining phase. A new prefix-tree data structure is developed to facilitate the storage and counting of the global candidate itemsets of different sizes. We built a Data Grid system on a cluster of workstations using the open-source Globus Toolkit, and evaluated the GridDMM algorithm in terms of performance, scalability, and the overhead of communication and synchronization. GridDMM demonstrates better performance than other sequential and parallel algorithms, and its performance is scalable in terms of the database size and the number of nodes. This research was supported in part by LexisNexis, NCR and AFRL/Wright Brothers Institute (WBI).     

不同品种大米理化性质及其淀粉结构对米饭食用品质的影响

米饭食味值和质构特性是评价大米食用品质的重要指标,为了探究不同品种大米的食味值、质构特性与其理化性质及淀粉结构的关系,以12种大米为研究样本,测定其食味值、质构特性、理化性质及淀粉结构特性,并分析内在相关性。结果表明,直链淀粉含量（质量分数）与米饭的硬度呈极显著正相关（P<0.01）,与米饭的凝聚性呈极显著负相关（P<0.01）;米饭食味值、质构特性与大米淀粉的糊化特性、热特性之间存在显著相关性,与结晶特性、淀粉颗粒表面有序度无显著相关性,表明淀粉的糊化特性决定了米饭的食味值、硬度、凝聚性和回复性。该研究可为后期优质稻米筛选培育以及大米适宜性加工提供理论基础。     

高含尘烟气电除尘器比集尘面积和电源匹配选择的探讨

为保证大型硫铁矿制酸装置净化后烟气含尘量＜0.15 g/m3,在电除尘器前不设旋风除尘器时要求除尘效率＞99.9%.探讨了影响比集尘面积的因素和选值原则,分析了处理高含尘烟气电除尘器应选用高阻电源或恒流电源,电除尘器才能获得最大功率,使其除尘效率达到或接近最高值.     

Text document clustering based on neighbors

Clustering is a very powerful data mining technique for topic discovery from text documents. The partitional clustering algorithms, such as the family of k-means, are reported performing well on document clustering. They treat the clustering problem as an optimization process of grouping documents into k clusters so that a particular criterion function is minimized or maximized. Usually, the cosine function is used to measure the similarity between two documents in the criterion function, but it may not work well when the clusters are not well separated. To solve this problem, we applied the concepts of neighbors and link, introduced in [S. Guha, R. Rastogi, K. Shim, ROCK: a robust clustering algorithm for categorical attributes, Information Systems 25 (5) (2000) 345–366], to document clustering. If two documents are similar enough, they are considered as neighbors of each other. And the link between two documents represents the number of their common neighbors. Instead of just considering the pairwise similarity, the neighbors and link involve the global information into the measurement of the closeness of two documents. In this paper, we propose to use the neighbors and link for the family of k-means algorithms in three aspects: a new method to select initial cluster centroids based on the ranks of candidate documents; a new similarity measure which uses a combination of the cosine and link functions; and a new heuristic function for selecting a cluster to split based on the neighbors of the cluster centroids. Our experimental results on real-life data sets demonstrated that our proposed methods can significantly improve the performance of document clustering in terms of accuracy without increasing the execution time much.     

Dynamic mechanical thermal analysis (DMTA) of aqueous phenol formaldehyde (PF) resin modified by nano copper oxide (CuO)

To improve the bio-resistance of engineered wood composites products via gluing process, aqueous phenol formaldehyde (PF) resin was modified using nano CuO containing alkane surfactant and polyvinyl alcohol (PVA) 17-99. The modified PF system was analyzed by dynamic mechanical thermal analysis, and the mechanical properties of the bonded plywood panels including tensile strength, modulus of rupture (MOR), modulus of elasticity (MOE) and shear strength under five test conditions were also evaluated. The results indicated that the addition of nano CuO incorporating PVA 17-99 separated the gel point and vitrification point in the curve of tan δ, which is related to the delaying of moisture loss in modified PF resin during the curing process. The modification showed adverse effect on tensile strength but only a minimal influence on MOR and MOE. Additionally, PVA 17-99 reduced the water resistance of cured PF resins. However, with the test conditions of dipping in 100 °C water for 6 h, then drying for 20 h at 63 °C in air, followed by dipping in 100 °C water for 4 h, PVA consolidated the re-curing effect on the PF resin and compensated the strength loss from hydrolysis. Thus, the modified PF system not only guaranteed bio-resistance of glued wood composites via CuO, but also has the potential for developing self-curing wood composites being applied as structural construction materials.     

米糠不溶性膳食纤维的提取及吸附铅离子探究

为促进米糠资源的综合利用,对米糠不溶性膳食纤维的提取方法及吸附Pb2+特性进行研究。采用酶-化学方法提取脱脂米糠中的不溶性膳食纤维。通过单因素和正交试验对高温淀粉酶添加量、NaOH质量分数、碱解时间和碱解温度对产物提取率和纯度的影响进行研究,确定制备米糠不溶性膳食纤维的较优参数条件。结果显示,在高温淀粉酶添加量0.3%,NaOH质量分数3%,75℃碱解75 min时,所得产物纯度85.74%,得率37.40%。吸附试验结果:提取物在20 mg/L Pb2+水溶液中振荡反应180 min时达到吸附平衡,此时吸附率为96.67%。动力学符合准二级动力学方程,能较好地描述米糠不溶性膳食纤维与Cd2+的结合行为,说明吸附过程存在化学吸附。热力学探究表明:25℃有利于吸附的进行,这一过程是熵驱动、自发进行的,同时吸附Pb2+强度较弱,推测作用力主要是物理吸附。米糠不溶性膳食纤维具有较好的吸附Pb2+能力。本试验为米糠膳食纤维材料的提取条件及其吸附Pb2+特性提供了理论依据。     

Incorporation of a nano/micro CuO formulation into phenol formaldehyde (PF) resin: Curing kinetics,morphological analysis,and application

The effects of incorporation of nano-CuO into phenol formaldehyde (PF) resin during sythesis on curing kinetics, nano-CuO structure and bond quality were investigated on orientated strand board (OSB). Apparent activation energy of the PF resins modified with CuO was decreased during the reaction indicating an acceleration of both addition and condensation reactions. X-ray diffraction data indicated that the Cu was in a bivalent state. Morphological analysis confirmed that CuO was uniformly dispersed at both nano and micro scales. Addition of nano-CuO to PF resin was associated with significantly higher internal bond strength on OSB panels. The results suggest that nano-CuO enhances the properties of conventional PF resin to imporve panel properties.     

医用非织造布的发展现状及相关建议

本文概述了世界医用非织造布的发展趋势和厂商动态，对我国医用非织造布的发展现状，应用前景和存在的问题了分析和阐述，并对促进我国医疗卫生用非织造布的发展和推广应用提出了相关建议。     

Efficient mining of maximal frequent itemsets from databases on a cluster of workstations

In this paper, we propose two parallel algorithms for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. One parallel algorithm is named distributed max-miner (DMM), and it requires very low communication and synchronization overhead in distributed computing systems. DMM has the local mining phase and the global mining phase. During the local mining phase, each node mines the local database to discover the local maximal frequent itemsets, then they form a set of maximal candidate itemsets for the top-down search in the subsequent global mining phase. A new prefix tree data structure is developed to facilitate the storage and counting of the global candidate itemsets of different sizes. This global mining phase using the prefix tree can work with any local mining algorithm. Another parallel algorithm, named parallel max-miner (PMM), is a parallel version of the sequential max-miner algorithm (Proc of ACM SIGMOD Int Conf on Management of Data, 1998, pp 85–93). Most of existing mining algorithms discover the frequent k-itemsets on the kth pass over the databases, and then generate the candidate (k + 1)-itemsets for the next pass. Compared to those level-wise algorithms, PMM looks ahead at each pass and prunes more candidate itemsets by checking the frequencies of their supersets. Both DMM and PMM were implemented on a cluster of workstations, and their performance was evaluated for various cases. They demonstrate very good performance and scalability even when there are large maximal frequent itemsets (i.e., long patterns) in databases.