Interrelation analysis of celestial spectra data using constrained frequent pattern trees

Association rule mining, in which generating frequent patterns is a key step, is an effective way of identifying inherent and unknown interrelationships between characteristics of celestial spectra data and its physicochemical properties. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. Finally, we quantitatively evaluate the CPU and I/O performance of our novel interrelation analysis method using a variety of real-world data sets. Our experimental results show that it is practical to study the laws of celestial bodies using our new interrelation analysis method to discover correlations between celestial spectra data characteristics and the physicochemical properties.

[1]  Chen An.  An Improved Algorithm Based on Maximum Clique and FP-Tree for Mining Association Rules , 2004 .

[2]  Zhang Ji-fu,et al.  A study on the outlier mining system for LAMOST spectra , 2007 .

[3]  Ravi K. Gulati,et al.  A COMPARISON OF SYNTHETIC AND OBSERVED SPECTRA FOR G-K DWARFS USING ARTIFICIAL NEURAL NETWORKS , 1997 .

[4]  Sanguthevar Rajasekaran,et al.  Ieee Transactions on Knowledge and Data Engineering 1 a Transaction Mapping Algorithm for Frequent Itemsets Mining , 2022 .

[5]  Gaspar Galaz,et al.  Spectral Classification of Galaxies with z ≲ 0.5 , 1997 .

[6]  A. Szalay,et al.  Spectral classification of galaxies: An Orthogonal approach , 1994, astro-ph/9411044.

[7]  Liu Rong.  Spectral Classification of Galaxy Based on Wavelet Feature , 2005 .

[8]  Jiawei Han,et al.  TFP: an efficient algorithm for mining top-k frequent closed itemsets , 2005, IEEE Transactions on Knowledge and Data Engineering.

[9]  Ann I. Zabludoff,et al.  Spectral classification of galaxies along the hubble sequence , 1995 .

[10]  Tang Chang-jie,et al.  An Improved Algorithm Based on Maximum Clique and FP-Tree for Mining Association Rules , 2004 .

[11]  Bala Srinivasan,et al.  Logic-Based Pattern Discovery , 2010, IEEE Transactions on Knowledge and Data Engineering.

[12]  J. D. Ponz,et al.  Automated classification of IUE low-dispersion spectra. I. Normal stars. , 1995 .

[13]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[14]  Ann Q. Gates,et al.  TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005 .

[15]  Ji-fu Zhang,et al.  [A study on the outlier mining system for LAMOST spectra]. , 2007, Guang pu xue yu guang pu fen xi = Guang pu.

[16]  Mohammed J. Zaki,et al.  Efficient algorithms for mining closed itemsets and their lattice structure , 2005, IEEE Transactions on Knowledge and Data Engineering.

[17]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[18]  Zhang Ji.  A Concept Lattice Based Recognition Method of Celestial Spectra Outliers , 2008 .

[19]  Archana Raghavendran,et al.  ITR: An Improved Algorithm For Mining Frequent Itemsets , 2007, DMIN.

[20]  Richard Stone,et al.  China's LAMOST Observatory Prepares for the Ultimate Test , 2008, Science.

[21]  Yong-heng Zhao,et al.  [A PCA based efficient stellar spectra classification method]. , 2003, Guang pu xue yu guang pu fen xi = Guang pu.

[22]  Philip S. Yu,et al.  Discovering Frequent Closed Partial Orders from Strings , 2006, IEEE Transactions on Knowledge and Data Engineering.

[23]  Ji-Fu Zhang.  A Concept Lattice Based Recognition Method of Celestial Spectra Outliers: A Concept Lattice Based Recognition Method of Celestial Spectra Outliers , 2009 .

[24]  Peter C. Cheeseman,et al.  Bayesian Classification (AutoClass): Theory and Results , 1996, Advances in Knowledge Discovery and Data Mining.

[25]  Salvatore Orlando,et al.  Fast and memory efficient mining of frequent closed itemsets , 2006, IEEE Transactions on Knowledge and Data Engineering.

[26]  Ehud Gudes,et al.  Discovering Frequent Graph Patterns Using Disjoint Paths , 2006, IEEE Transactions on Knowledge and Data Engineering.

[27]  Sanjit K. Mitra,et al.  Two-dimensional digital signal processing , 1978 .

[28]  Harinder P. Singh,et al.  Stellar spectral classification using principal component analysis and artificial neural networks , 1998 .

[29]  Thomas S. Huang,et al.  Two-Dimensional Digital Signal Processing I , 1981 .

[30]  Royal Greenwich Observatory,et al.  Physical parametrization of stellar spectra: the neural network approach , 1997 .

[31]  Salvatore Ruggieri.  Frequent regular itemset mining , 2010, KDD '10.

[32]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[33]  K. Taylor,et al.  The 2dF Galaxy Redshift Survey: spectral types and luminosity functions , 1999, astro-ph/9903456.

[34]  Wm. Bruce Weaver,et al.  Accurate Two-Dimensional Classification of Stellar Spectra with Artificial Neural Networks , 1997 .