Class information for: |
Basic class information |
Class id | #P | Avg. number of references |
Database coverage of references |
---|---|---|---|
36433 | 81 | 30.6 | 54% |
Hierarchy of classes |
The table includes all classes above and classes immediately below the current class. |
Cluster id | Level | Cluster label | #P |
---|---|---|---|
10 | 4 | OPTICS//PHYSICS, PARTICLES & FIELDS//PHYSICS, MULTIDISCIPLINARY | 1131262 |
311 | 3 | SERS//SURFACE ENHANCED RAMAN SCATTERING//CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 40151 |
322 | 2 | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS//NEAR INFRARED SPECTROSCOPY//JOURNAL OF CHEMOMETRICS | 18229 |
36433 | 1 | ARYLPROPARGYL ETHERS OF PHENOLS//CHEMOMETR//SUPERVISED KOHONEN NETWORKS | 81 |
Terms with highest relevance score |
rank | Term | termType | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|---|
1 | ARYLPROPARGYL ETHERS OF PHENOLS | authKW | 1130951 | 4% | 100% | 3 |
2 | CHEMOMETR | address | 769936 | 28% | 9% | 23 |
3 | SUPERVISED KOHONEN NETWORKS | authKW | 753967 | 2% | 100% | 2 |
4 | APPLE SPOILAGE | authKW | 376984 | 1% | 100% | 1 |
5 | ASYMMETRICALLY SUBSTITUTED 1 1 BIS FERROCENE | authKW | 376984 | 1% | 100% | 1 |
6 | AUTHENTICATION EDIBLE OILS | authKW | 376984 | 1% | 100% | 1 |
7 | AUTHORSHIP ISSUES | authKW | 376984 | 1% | 100% | 1 |
8 | COLLABORATIVE GUIDELINES | authKW | 376984 | 1% | 100% | 1 |
9 | COUNTERPROPAGATION ARTIFICIAL NEURAL NETWORKS CP ANN | authKW | 376984 | 1% | 100% | 1 |
10 | D PLS | authKW | 376984 | 1% | 100% | 1 |
Web of Science journal categories |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | Chemistry, Analytical | 1652 | 60% | 0% | 49 |
2 | Automation & Control Systems | 1386 | 27% | 0% | 22 |
3 | Mathematics, Interdisciplinary Applications | 1259 | 28% | 0% | 23 |
4 | Statistics & Probability | 994 | 27% | 0% | 22 |
5 | Computer Science, Artificial Intelligence | 946 | 28% | 0% | 23 |
6 | Instruments & Instrumentation | 713 | 30% | 0% | 24 |
7 | Computer Science, Interdisciplinary Applications | 52 | 7% | 0% | 6 |
8 | Crystallography | 35 | 6% | 0% | 5 |
9 | Chemistry, Multidisciplinary | 18 | 12% | 0% | 10 |
10 | Engineering, General | 14 | 4% | 0% | 3 |
Address terms |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | CHEMOMETR | 769936 | 28% | 9% | 23 |
2 | DEPARTIMENTO CHIM VIA VIENNA 2 | 376984 | 1% | 100% | 1 |
3 | ORGAN SYNTH COAL CHEM | 201051 | 5% | 13% | 4 |
4 | REAL DISPLAY DEVICE SECT | 125660 | 1% | 33% | 1 |
5 | SENSORY BIOL MOL BIOL GENET | 125660 | 1% | 33% | 1 |
6 | FINANCIAL RISK INTELLIGENT CONTROL PREVENT | 94244 | 1% | 25% | 1 |
7 | PROD ASSURANCE PONSE | 94244 | 1% | 25% | 1 |
8 | MILANO CHEMOMETR QSAR GRP | 87409 | 5% | 6% | 4 |
9 | IST ANAL TECNOL FARMACEUT ALIMENTARI | 53853 | 1% | 14% | 1 |
10 | FINANCE MATH | 47121 | 1% | 13% | 1 |
Journals |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | JOURNAL OF CHEMOMETRICS | 22369 | 11% | 1% | 9 |
2 | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 20085 | 16% | 0% | 13 |
3 | CRITICAL REVIEWS IN ANALYTICAL CHEMISTRY | 3184 | 2% | 0% | 2 |
4 | CIRCUIT WORLD | 1892 | 1% | 1% | 1 |
5 | QUIMICA ANALITICA | 1183 | 1% | 0% | 1 |
6 | RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY A | 827 | 4% | 0% | 3 |
7 | ANALYTICA CHIMICA ACTA | 759 | 9% | 0% | 7 |
8 | CURRENT ANALYTICAL CHEMISTRY | 726 | 1% | 0% | 1 |
9 | ANALYST | 641 | 6% | 0% | 5 |
10 | RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY | 491 | 2% | 0% | 2 |
Author Key Words |
Core articles |
The table includes core articles in the class. The following variables is taken into account for the relevance score of an article in a cluster c: (1) Number of references referring to publications in the class. (2) Share of total number of active references referring to publications in the class. (3) Age of the article. New articles get higher score than old articles. (4) Citation rate, normalized to year. |
Rank | Reference | # ref. in cl. |
Shr. of ref. in cl. |
Citations |
---|---|---|---|---|
1 | BRERETON, RG , (2012) SELF ORGANISING MAPS FOR VISUALISING AND MODELLING.CHEMISTRY CENTRAL JOURNAL. VOL. 6. ISSUE . P. - | 13 | 57% | 6 |
2 | BRERETON, RG , (2011) ONE-CLASS CLASSIFIERS.JOURNAL OF CHEMOMETRICS. VOL. 25. ISSUE 5. P. 225 -246 | 10 | 48% | 31 |
3 | WONGRAVEE, K , LLOYD, GR , SILWOOD, CJ , GROOTVELD, M , BRERETON, RG , (2010) SUPERVISED SELF ORGANIZING MAPS FOR CLASSIFICATION AND DETERMINATION OF POTENTIALLY DISCRIMINATORY VARIABLES: ILLUSTRATED BY APPLICATION TO NUCLEAR MAGNETIC RESONANCE METABOLOMIC PROFILING.ANALYTICAL CHEMISTRY. VOL. 82. ISSUE 2. P. 628-638 | 11 | 44% | 14 |
4 | BRERETON, RG , SAGI-KISS, V , (2012) COMMENTS ON MULTIPLE SELF ORGANISING MAPS (MSOMS) FOR SIMULTANEOUS CLASSIFICATION AND PREDICTION: ILLUSTRATED BY SPOILAGE IN APPLES USING VOLATILE ORGANIC PROFILES BY SF SIM AND V. SAGI-KISS.CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. VOL. 118. ISSUE . P. 308-310 | 5 | 83% | 0 |
5 | BRERETON, RG , LLOYD, GR , (2010) SUPPORT VECTOR MACHINES FOR CLASSIFICATION AND REGRESSION.ANALYST. VOL. 135. ISSUE 2. P. 230 -267 | 10 | 34% | 84 |
6 | BALLABIO, D , VASIGHI, M , (2012) A MATLAB TOOLBOX FOR SELF ORGANIZING MAPS AND SUPERVISED NEURAL NETWORK LEARNING STRATEGIES.CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. VOL. 118. ISSUE . P. 24-32 | 6 | 55% | 16 |
7 | BALLABIO, D , VASIGHI, M , FILZMOSER, P , (2013) EFFECTS OF SUPERVISED SELF ORGANISING MAPS PARAMETERS ON CLASSIFICATION PERFORMANCE.ANALYTICA CHIMICA ACTA. VOL. 765. ISSUE . P. 45 -53 | 11 | 31% | 2 |
8 | DIXON, SJ , HEINRICH, N , HOLMBOE, M , SCHAEFER, ML , REED, RR , TREVEJO, J , BRERETON, RG , (2009) APPLICATION OF CLASSIFICATION METHODS WHEN GROUP SIZES ARE UNEQUAL BY INCORPORATION OF PRIOR PROBABILITIES TO THREE COMMON APPROACHES: APPLICATION TO SIMULATIONS AND MOUSE URINARY CHEMOSIGNALS.CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. VOL. 99. ISSUE 2. P. 111-120 | 6 | 60% | 3 |
9 | LI, D , LLOYD, GR , DUNCAN, JC , BRERETON, RG , (2010) DISJOINT HARD MODELS FOR CLASSIFICATION.JOURNAL OF CHEMOMETRICS. VOL. 24. ISSUE 5-6. P. 273-287 | 9 | 35% | 3 |
10 | LUKASIAK, BM , DUNCAN, JC , (2010) CLASSIFICATION OF POLYMER GROUPS BY MEANS OF A NEW POLYMER TESTING INSTRUMENT, THE IDENTIPOL QA, COUPLED WITH PATTERN RECOGNITION TECHNIQUES.ANALYTICAL METHODS. VOL. 2. ISSUE 12. P. 1948 -1957 | 8 | 36% | 1 |
Classes with closest relation at Level 1 |