Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

Basket Analysis, which is a standard method for data mining, derives frequent itemsets from database. However, its mining ability is limited to transaction data consisting of items. In reality, there are many applications where data are described in a more structural way, e.g. chemical compounds and Web browsing history. There are a few approaches that can discover characteristic patterns from graph-structured data in the field of machine learning. However, almost all of them are not suitable for such applications that require a complete search for all frequent subgraph patterns in the data. In this paper, we propose a novel principle and its algorithm that derive the characteristic patterns which frequently appear in graph-structured data. Our algorithm can derive all frequent induced subgraphs from both directed and undirected graph structured data having loops (including self-loops) with labeled or unlabeled nodes and links. Its performance is evaluated through the applications to Web browsing pattern analysis and chemical carcinogenesis analysis.     

Multi-structure information retrieval method based on transformation invariance

The needs of efficient and flexible information retrieval on multi-structural data stored in database and network are significantly growing. Especially, its flexibility plays one of the key roles to acquire relevant information desired by users in retrieval process. However, most of the existing approaches are dedicated to a single content and data structure respectively, e.g., relational database and natural text. In this work, we propose “Multi-Structure Information Retrieval” (MSIR) approach applicable to various types of contents and data structures by adapting a small part of the approach to data structures. The power of this approach comes from the use of the invariant feature information obtained from byte patterns in the files through some mathematical transformation. The experimental evaluation of the proposed approach for both artificial and real data indicates its high feasibility. Fuminori Adachi: He received his Master of engineering from Osaka University in ’03. He is enrolled in the doctoral course of Osaka University from ’03. His current research interest includes scientific discovery, data mining and machine learning techniques. Takashi Washio, Ph.D.: He received his Ph.D. from Tohoku University in ’88. In ’88, he became a visiting reseacher in Massachusetts Institute of Technology. In ’90, he joined Mitsubishi Research Institute Inc., and is working for Osaka University from ’96. His current research interest includes scientific discovery, data mining and machine learning techniques. Atsushi Fujimoto: He is enrolled in the master cource of Osaka University from ’03. His Current research interest includes correlation analysis, data mining and machine learning techniques. Hiroshi Motoda, Ph.D.: He received his Ph.D. from University of Tokyo in ’72. In ’67, he joined Hitachi Ltd. and has been working for Osaka University since ’96. His current research interest includes scientific discovery, data mining and machine learning. Hidemitsu Hanafusa: He received Master of Engineering from Keio University in ’83. In ’83, he joined The Kansai Electric Power Co. Ins. (KEPCO). He researched on Maintenance Support System at INSS from ’97 to ’02. Now, he is working in KEPCO.     

Radiation-induced polymerization of ethylene in a pilot plant. I. Bulk process

Radiation-induced bulk polymerization of ethylene was carried out with use of a pilot plant with a 10 liter reactor at pressures of 225–400 kg/cm², temperatures of 30–95°C, ethylene feed rates of 5–28 kg/hr, and dose rate of 3.8 × 10⁵ rad/hr. Characteristics of the process are mild polymerization conditions and capability of producing medium density polyethylene in powder form. The spacetime yield and molecular weight of polymer were in the range of 3.5 to 13.1 g/liter hr and 2.2 × 10⁴ to 14 × 10⁴, respectively. The space-time yield increased with mean residence time and 2.4 powders of pressure, and decreased with temperature. Molecular weight changed similarly with the reaction conditions. These results were consistent with those of the bench plant experiment and the scale effect was small. Polymer deposit to the reactor wall limited a period of continuous operation of the plant. The amount of deposited polymer was increased with the square of reaction time. The rate of polymer deposit was proportional to polymer concentration and to the cube of pressure. The polymer deposit cannot be solved in the bulk process.     

An estimation of friction from the indentation of metals by acute-angled conical indenters

The paper describes a method for estimating the coefficient of friction (COF) between sliding surfaces by an indentation of an acute-angled indenter. The COF estimated by the method is compared with the COF measured by a friction tester. The conical indenter, which is made of WC and has an apex angle of 45°, is used for the experiments. A micro-Vickers hardness tester is used for the indentation tests. The friction tester used is one of Bowden–Leben type. A copper and a 0.45% carbon steel are used for the indentation and sliding specimens. The estimation of the COF is based on the equilibrium equation of indentation, which takes the friction between the contacting surfaces of the indenter and the specimen into consideration. The results show that the estimated COF is approximately equal to the measured one. Based on the results, the validity of this method is discussed.     

Purification and characterization of polyphenol oxidase from Trametes sp. MS39401

Two polyphenol oxidases (EC 1.14.18.1), P-1 and P-2, were purified as electrophoretically homogeneous proteins from the culture filtrate of Trametes sp. MS39401 by acetone precipitation and column chromatographies on DEAE-Sephadex A-50, Sephadex G-150 and hydroxylapatite. P-1 was purified 34-fold with a yield of 4.2%, while P-2 was purified 37-fold with a yield of 20.7%. The molecular masses of P-1 and P-2 were estimated to be 61 kDa and 90 kDa, respectively, by gel filtration. The isoelectric points of P-1 and P-2 were 3.4 and 2.7, respectively. The optimum pH range of both enzymes was 4.5-5.0 at 45 degrees C. The optimum temperature of both enzymes was 55 degrees C at pH 5.0. P-1 was stable at pH 5.0-7.5 and temperatures up to 60 degrees C. P-2 was stable at pH 3.0-7.5 and temperatures up to 50 degrees C. The thermostability of P-1 was comparable to that of the PM1 laccase of basidiomycetes, which was reported to be the most stable among basidiomycete laccases. Both enzymes were active toward various phenolic compounds and aminophenols. However, they lacked activity toward l-tyrosine. The K(m) values for (+)-catechin were 0.19 mM for P-1 and 0.67 mM for P-2. Both enzymes were appreciably inactivated by Hg(2+) and Sn(2+). Significant activation of neither enzyme was observed in the presence of metal ions and reagents. Both enzymes were significantly inhibited by copper-chelating agents, reducing agents and N-bromosuccinimide. Carbon monoxide caused appreciable inactivation of neither enzyme, so it is suggested that P-1 and P-2 belong to the group of laccases.     

DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm

In this paper, we present a new tree mining algorithm, DryadeParent, based on the hooking principle first introduced in DRYADE. In the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factors of complexity for tree mining algorithms, even if often overlooked in previous work. We show that DryadeParent outperforms the current fastest algorithm, CMTreeMiner, by orders of magnitude on data sets where the frequent tree patterns have a high branching factor.