Sparse Keyword Data Analysis Using Bayesian Pattern Mining
Abstract
1. Introduction
2. Research Background
2.1. Keyword Analysis
2.2. Association Rule Mining
3. Bayesian Pattern Mining for Sparse Keyword Data Analysis
Algorithm 1 Building Document–Keyword Matrix |
Input: - Document data - Unique string separator |
Output: - Extracted keywords - Document–keyword matrix 1. Collecting text data - documents, papers, patents, reports, news articles, legal documents - social media and chat comments, online comments 2. Checking data schema - removing duplicated documents - missing value imputation 3. Normalizing text data - removing spaces, numbers, symbols, etc. - lowercasing, using stopwords by user dictionary 4. Lemmatization and stemming 5. Parsing - corpus, text database 6. Constructing document–keyword matrix - sparse matrix, keyword extraction - each element representing frequency of keywords occurring in each document 7. Changing element value from count to binary - binary document–keyword matrix - using binary matrix for transaction data for association rule mining |
Algorithm 2 Bayesian Pattern Mining |
Input: - sparse document–keyword matrix with binary data |
Output: - means and credible intervals of association rules 1. Constructing 2 × 2 contingency table - X: antecedent item (keyword) - Y: consequent item (keyword) 2. Building posterior distribution - prior: - likelihood: - posterior: 3. Obtaining interesting measures - support: - confidence: - lift: 4. Estimating mean and credible interval - drawing samples from posterior distribution - estimating probability values, expectation, and credible interval |
Algorithm 3 Bayesian Inference for Association Rules |
Input: - of contingency table - prior, likelihood (M samples), posterior - threshold of confidence |
Output: - summary statistics of confidence and lift - credible intervals of confidence and lift - probability values of and 1. Obtaining parameters for posterior distribution - for all elements 2. Drawing M samples from posterior distribution - posterior: 3. Computing interesting measures per sample - - - 4. Estimating summary statistics - calculating posterior mean and 95% credible intervals - estimating probability values of |
4. Experimental Results
5. Discussion and Implications
5.1. Theoretical Implications
5.2. Practical Implications
5.3. Differentiation from Existing Work
6. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
References
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Item | Row Sum | ||
---|---|---|---|
Column sum | Total sum = N |
Technology Category | Representative Keywords |
---|---|
Core | algorithm, bit, calcul, circuit, comput, entangl, error, gate, Hamiltonian, ion, measur, photon, quantum, qubit, sequenc, signal, simul, state, superconduct, timetrap, |
Software | algorithm, authent, circuit, cloud, code, connect, data, electron, encrypt, execut, function, gate, graph, implement, includ, inform, key, logic, model, modul, network, optim, perform, processor, program, random, sampl, secret, secur, simul, structur, technolog, |
Hardware | chip, electron, energi, ion, memori, optic, optical, photon, reson, storage, substrat, superconduct, trap |
LHS | RHS | Confidence | Lift | Support |
---|---|---|---|---|
bit | quantum | 0.9809 | 1.0777 | 0.1271 |
photon | quantum | 0.9806 | 1.0773 | 0.0379 |
chip | quantum | 0.9766 | 1.0730 | 0.0761 |
atom | quantum | 0.9714 | 1.0673 | 0.0255 |
superconduct | quantum | 0.9659 | 1.0612 | 0.0669 |
processor | quantum | 0.9596 | 1.0543 | 0.0995 |
qubit | quantum | 0.9494 | 1.0431 | 0.2134 |
logic | quantum | 0.9493 | 1.0430 | 0.0643 |
program | quantum | 0.9489 | 1.0425 | 0.0717 |
hybrid | quantum | 0.9424 | 1.0354 | 0.0491 |
computing | quantum | 0.9402 | 1.0329 | 0.7622 |
electron | quantum | 0.9330 | 1.0251 | 0.0792 |
code | quantum | 0.9266 | 1.0181 | 0.0583 |
hybrid | computing | 0.9239 | 1.1396 | 0.0482 |
connect | quantum | 0.9237 | 1.0148 | 0.0870 |
chip | computing | 0.9118 | 1.1248 | 0.0710 |
memory | quantum | 0.9000 | 0.9888 | 0.0454 |
inform | quantum | 0.8997 | 0.9885 | 0.1493 |
network | quantum | 0.8986 | 0.9872 | 0.1150 |
program | computing | 0.8949 | 1.1038 | 0.0676 |
calculation | quantum | 0.8919 | 0.9800 | 0.1603 |
cloud | computing | 0.8857 | 1.0925 | 0.0299 |
vector | quantum | 0.8834 | 0.9706 | 0.0325 |
cloud | quantum | 0.8762 | 0.9627 | 0.0296 |
atom | computing | 0.8735 | 1.0774 | 0.0230 |
superconduct | computing | 0.8698 | 1.0728 | 0.0602 |
data | quantum | 0.8683 | 0.9540 | 0.2066 |
processor | computing | 0.8654 | 1.0675 | 0.0897 |
electron | computing | 0.8546 | 1.0542 | 0.0725 |
qubit | computing | 0.8535 | 1.0527 | 0.1919 |
LHS | RHS | Lift | Confidence | Support |
---|---|---|---|---|
encryption | security | 5.9356 | 0.5567 | 0.0411 |
security | encryption | 5.9356 | 0.4382 | 0.0411 |
random | encryption | 4.9903 | 0.3684 | 0.0203 |
encryption | random | 4.9903 | 0.2747 | 0.0203 |
processor | memory | 4.9056 | 0.2474 | 0.0256 |
memory | processor | 4.9056 | 0.5085 | 0.0256 |
atom | photon | 4.3320 | 0.1673 | 0.0044 |
photon | atom | 4.3320 | 0.1139 | 0.0044 |
superconduct | chip | 4.1792 | 0.3256 | 0.0225 |
chip | superconduct | 4.1792 | 0.2893 | 0.0225 |
encryption | cloud | 3.6980 | 0.1250 | 0.0092 |
cloud | encryption | 3.6980 | 0.2730 | 0.0092 |
random | security | 3.6373 | 0.3411 | 0.0188 |
security | random | 3.6373 | 0.2002 | 0.0188 |
cloud | random | 3.0565 | 0.1683 | 0.0057 |
random | cloud | 3.0565 | 0.1033 | 0.0057 |
memory | logic | 2.8909 | 0.1957 | 0.0099 |
logic | memory | 2.8909 | 0.1458 | 0.0099 |
chip | connect | 2.8801 | 0.2713 | 0.0211 |
connect | chip | 2.8801 | 0.2244 | 0.0211 |
security | cloud | 2.8772 | 0.0973 | 0.0091 |
cloud | security | 2.8772 | 0.2698 | 0.0091 |
chip | bit | 2.7757 | 0.3595 | 0.0280 |
bit | chip | 2.7757 | 0.2162 | 0.0280 |
processor | hybrid | 2.6400 | 0.1377 | 0.0143 |
hybrid | processor | 2.6400 | 0.2737 | 0.0143 |
superconduct | connect | 2.6000 | 0.2450 | 0.0170 |
connect | superconduct | 2.6000 | 0.1800 | 0.0170 |
memory | program | 2.3940 | 0.1809 | 0.0091 |
program | memory | 2.3940 | 0.1207 | 0.0091 |
LHS | RHS | Credible Interval of Confidence Measure | Lift | Support | ||
---|---|---|---|---|---|---|
Mean | Lower | Upper | Mean | Mean | ||
bit | quantum | 0.9804 | 0.9719 | 0.9882 | 1.0773 | 0.1271 |
photon | quantum | 0.9793 | 0.9631 | 0.9910 | 1.0760 | 0.0379 |
chip | quantum | 0.9760 | 0.9636 | 0.9855 | 1.0724 | 0.0761 |
atom | quantum | 0.9696 | 0.9443 | 0.9879 | 1.0654 | 0.0255 |
superconduct | quantum | 0.9652 | 0.9496 | 0.9778 | 1.0605 | 0.0669 |
processor | quantum | 0.9590 | 0.9459 | 0.9701 | 1.0538 | 0.0995 |
qubit | quantum | 0.9493 | 0.9392 | 0.9584 | 1.0430 | 0.2134 |
logic | quantum | 0.9487 | 0.9312 | 0.9647 | 1.0425 | 0.0643 |
program | quantum | 0.9480 | 0.9303 | 0.9633 | 1.0417 | 0.0717 |
hybrid | quantum | 0.9416 | 0.9192 | 0.9602 | 1.0345 | 0.0491 |
computing | quantum | 0.9401 | 0.9347 | 0.9455 | 1.0330 | 0.7622 |
electron | quantum | 0.9327 | 0.9159 | 0.9492 | 1.0250 | 0.0792 |
code | quantum | 0.9261 | 0.9053 | 0.9443 | 1.0175 | 0.0583 |
hybrid | computing | 0.9231 | 0.8977 | 0.9454 | 1.1388 | 0.0482 |
connect | quantum | 0.9230 | 0.9047 | 0.9399 | 1.0142 | 0.0870 |
chip | computing | 0.9117 | 0.8904 | 0.9312 | 1.1246 | 0.0710 |
inform | quantum | 0.8993 | 0.8836 | 0.9137 | 0.9883 | 0.1493 |
memory | quantum | 0.8992 | 0.8710 | 0.9243 | 0.9879 | 0.0454 |
network | quantum | 0.8983 | 0.8814 | 0.9152 | 0.9870 | 0.1150 |
program | computing | 0.8947 | 0.8716 | 0.9155 | 1.1036 | 0.0676 |
calculation | quantum | 0.8917 | 0.8764 | 0.9065 | 0.9799 | 0.1603 |
cloud | computing | 0.8839 | 0.8466 | 0.9171 | 1.0903 | 0.0299 |
vector | quantum | 0.8825 | 0.8450 | 0.9145 | 0.9696 | 0.0325 |
cloud | quantum | 0.8745 | 0.8362 | 0.9075 | 0.9608 | 0.0296 |
atom | computing | 0.8721 | 0.8282 | 0.9104 | 1.0757 | 0.0230 |
superconduct | computing | 0.8693 | 0.8413 | 0.8944 | 1.0721 | 0.0602 |
data | quantum | 0.8680 | 0.8540 | 0.8819 | 0.9539 | 0.2066 |
processor | computing | 0.8652 | 0.8416 | 0.8863 | 1.0674 | 0.0897 |
electron | computing | 0.8538 | 0.8282 | 0.8780 | 1.0531 | 0.0725 |
qubit | computing | 0.8532 | 0.8373 | 0.8679 | 1.0526 | 0.1919 |
LHS | RHS | Credible Interval of Lift Measure | Confidence | Support | ||
---|---|---|---|---|---|---|
Mean | Lower | Upper | Mean | Mean | ||
security | encryption | 5.9397 | 5.5412 | 6.3394 | 0.4380 | 0.0411 |
encryption | security | 5.9312 | 5.5399 | 6.3563 | 0.5568 | 0.0411 |
encryption | random | 4.9807 | 4.4713 | 5.5169 | 0.2744 | 0.0203 |
random | encryption | 4.9768 | 4.4599 | 5.5109 | 0.3680 | 0.0203 |
processor | memory | 4.9091 | 4.4942 | 5.3458 | 0.2478 | 0.0256 |
memory | processor | 4.9009 | 4.4858 | 5.3185 | 0.5086 | 0.0256 |
chip | superconduct | 4.1867 | 3.7600 | 4.6149 | 0.2902 | 0.0225 |
superconduct | chip | 4.1830 | 3.7522 | 4.6094 | 0.3263 | 0.0225 |
security | random | 3.6474 | 3.2506 | 4.0597 | 0.2012 | 0.0188 |
random | security | 3.6309 | 3.2322 | 4.0481 | 0.3406 | 0.0188 |
connect | chip | 2.8765 | 2.5829 | 3.1906 | 0.2244 | 0.0211 |
chip | connect | 2.8743 | 2.5706 | 3.2114 | 0.2708 | 0.0211 |
bit | chip | 2.7764 | 2.5242 | 3.0326 | 0.2164 | 0.0280 |
chip | bit | 2.7754 | 2.5363 | 3.0249 | 0.3598 | 0.0280 |
processor | hybrid | 2.6458 | 2.2926 | 3.0125 | 0.1382 | 0.0143 |
hybrid | processor | 2.6424 | 2.2901 | 2.9987 | 0.2741 | 0.0143 |
superconduct | connect | 2.6076 | 2.3019 | 2.9425 | 0.2460 | 0.0170 |
connect | superconduct | 2.6054 | 2.2929 | 2.9412 | 0.1806 | 0.0170 |
data | cloud | 2.3239 | 2.0822 | 2.5532 | 0.0788 | 0.0187 |
cloud | data | 2.3211 | 2.0941 | 2.5495 | 0.5520 | 0.0187 |
network | security | 2.2942 | 2.0781 | 2.5122 | 0.2154 | 0.0275 |
security | network | 2.2903 | 2.0867 | 2.5047 | 0.2930 | 0.0275 |
encryption | data | 2.1150 | 1.9642 | 2.2703 | 0.5033 | 0.0371 |
data | encryption | 2.1129 | 1.9585 | 2.2696 | 0.1561 | 0.0371 |
logic | qubit | 2.0029 | 1.8417 | 2.1603 | 0.4504 | 0.0305 |
qubit | logic | 2.0005 | 1.8366 | 2.1706 | 0.1357 | 0.0305 |
security | inform | 1.9195 | 1.7500 | 2.0927 | 0.3184 | 0.0298 |
inform | security | 1.9175 | 1.7436 | 2.0918 | 0.1800 | 0.0298 |
encryption | network | 1.9101 | 1.6762 | 2.1574 | 0.2445 | 0.0180 |
network | encryption | 1.9085 | 1.6822 | 2.1442 | 0.1412 | 0.0180 |
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Jun, S. Sparse Keyword Data Analysis Using Bayesian Pattern Mining. Computers 2025, 14, 436. https://doi.org/10.3390/computers14100436
Jun S. Sparse Keyword Data Analysis Using Bayesian Pattern Mining. Computers. 2025; 14(10):436. https://doi.org/10.3390/computers14100436
Chicago/Turabian StyleJun, Sunghae. 2025. "Sparse Keyword Data Analysis Using Bayesian Pattern Mining" Computers 14, no. 10: 436. https://doi.org/10.3390/computers14100436
APA StyleJun, S. (2025). Sparse Keyword Data Analysis Using Bayesian Pattern Mining. Computers, 14(10), 436. https://doi.org/10.3390/computers14100436