Index
F1 score, 302, 311
K-means clustering, 17, 242, 245, 351,355
K-nearest neighbor graph, 97
K-nearest neighbors (KNN), 42, 90
L1 norm, 37, 99, 256, 340, 344
L2 norm, 36, 37, 54–56, 59, 67, 95, 99,256, 266, 324, 340, 344, 356
Lp norm for vectors, 340
χ2 test, 304
δ-tolerance closed frequent pattern mining, 228, 235
t-test, 94

Bayes estimation, 353
Bayes theorem, 25, 132, 353
Bayesian belief network (BBN), 32
Bayesian belief network classifier, 53
Bayesian learning, 127
Bernoulli distribution, 327
Bernoulli trial, 326
beta distribution, 328
bi-factorization, 35
bicluster, 32
biclustering, 32, 110
binary classification, 42, 117
binary tree, 27
Binomial distribution, 327
accuracy, 300
boosting, 83
AdaBoost, 81
bootstrap aggregating (bagging), 74
adjacency matrix, 20, 96, 109, 238, 248, bootstrapping, 73, 296
260, 344
breadth-first search (BFS), 191
adjusted rand index (ARI), 314
agglomerative hierarchical clustering, 27, candidate generation, 122, 146
29, 184
canonical correlation analysis (CCA), 265,
all subsequence kernel, 170, 209351
all subtree kernel, 208, 211
canonical form, 209
alternating least squares, 347
case-based reasoning (CBR), 43, 90
alternating least squares (ALS) algorithm, Cholesky decomposition, 336
36, 55, 56, 249, 250
classification, 42, 115
Apriori algorithm, 122closed frequent pattern mining, 235
area under the precision-recall (AUPR), closed frequent patternmining, 228311
cluster assignment matrix, 18, 40, 101,
area under the ROC curve (AUC), 46,
240, 242, 248
94, 308
clustering, 4, 16, 40
artificial neural network, see layered neu- collaborative matrix factorization, 288
ral network
computational linguistics, 8
conditional random field (CRF), 168
back-propagation (BP), 67
conjugate prior distribution, 133, 354
bag of words (BOW), 119, 127, 152connected subgraph, 11
Baum-Welch algorithm, 159, 166

INDEX
constrained K-means clustering, 23, 98,102, 244, 246, 285
context free grammar (CFG), 164
contingency table, 298
convex optimization, 346
correlation coeﬃcient, 94, 330
covariance, 329, 363
covariance matrix, 96, 104, 269
cross-validation, 109, 296

372
expected reciprocal rank (ERR), 321feature learning, 91
feature selection, 91
feature vector, 7
feed-forward neural network, see layered
neural network
finite mixture model (FMM), 24, 153
Fisher discriminant analysis, 351
Fisher’s exact test, 304
data fusion, 13
FP-growth algorithm, 123, 147, 151data sparsity, 5, 8, 239
frequent itemset mining, 120, 144, 147data transformation, 13
frequent pair mining, 275
decision stump, 44, 92, 98
frequent pattern mining, 120
decision tree, 47, 63, 74, 75, 98, 116
frequent subgraph mining, 220, 226, 231
deep learning, 70, 72
frequent subsequence mining, 145, 146,
dendrogram, 27, 28, 110
178
depth-first search (DFS), 149, 150, 191, frequent subtree mining, 210, 216
221
Frobenius norm, 341, 342
dimensionality reduction, 92
full rank, 34
directed acyclic graph, 33
Dirichlet distribution, 130, 131, 133, 328 G-protein coupled receptor(GPCR), 114
discriminative model, 26, 62, 169
geneexpression,
110, 115, 119, 279
divisive hierarchical clustering, 27
generegulatory
network,
12, 237
downward closure property, 121, 146,
gene
setenrichment
analysis(GSEA),
220, 276
113
drug-target interaction, 111
dynamic programming, 158, 162, 182, generalization, 42, 48
generalized Rayleigh quotient, 98
184, 185, 197, 202, 277
generalized CCA, 268
E-commerce (electric commerce), 5, 8, generalized eigenvalue problem, 97, 102,15, 121, 261, 263
244, 267, 268, 272, 334, 351
eigenvalue decomposition, see eigenvalue generalized linear model, 56
problemgeneralized Rayleigh quotient, 23
eigenvalue problem, 22, 23, 34, 40, 95, generalized sequential patterns (GSP)
97, 98, 108, 242, 260, 267, 286,algorithm, 146, 147
333
generative model, 26, 32, 62, 127, 131,
elastic net, 100, 256, 270
169
ensemble learning, 63, 64, 72
Gibbs sampling, 132, 354
entropy, 45, 325, 331
Gini index, 309
enumeration tree, 225, 231
glycan, 10, 212
error rate, 301
gradient descent, 58, 59, 67, 68, 169
exclusive OR (XOR), 62
graph, 6, 9, 10, 12, 96, 109, 219
Expectation and Maximization (EM) al- graph cut, 242
gorithm, 25, 128, 155graph kernel, 229, 231
Expectation-Maximization (EM) algo- graph Laplacian, 97, 98, 241, 248, 287,rithm, 159, 352
288, 344, 346

INDEXgraph learning, 237, 239
graph partitioning, 238, 240, 260
graphical model, 33, 52, 127, 131, 156,
192
greedy algorithm, 29, 31
gSpan algorithm, 221

373

Laplacian eigenmaps, 92, 96, 247, 282
latent Dirichlet allocation (LDA), 127
latent variable, 4, 24, 127, 131, 132, 135,153, 154, 169
layered neural network, 64, 67, 69, 72,358
least absolute shrinkage and selection
hard margin, 85
operator (LASSO), 100, 256
hidden Markov model (HMM), 154
least squares, 347
hidden Markov model (HMM), 156, 184, likelihood, 58, 128
186, 188, 193
linear regression, 54, 99
hidden tree Markov model, 213
linkage, 29
hidden tree Markov model (HTMM), 193, logistic function, see sigmoid function
206
logistic regression, 58, 60, 62, 64, 66, 67,
hidden variable, see latent variable
69, 72, 99, 169, 357
hierarchical clustering, 16, 27, 110
low-rank approximation, see matrix fachigh-throughput data, 5, 115, 237, 279
torization
Hilbert-Schmidt norm, see Frobenius norm
hyperplane, 84majority voting, 42
margin, 84
information, 45
market basket, 8, 119, 121, 145
information gain, 45
Markov blanket, 93
inner product, 89, 325, 329
Markov model, 153
inner product space, 336
Markov property, 152
invertible matrix, see nonsingular ma- match kernel, 136
trixmatrix completion, 41
matrix factorization, 34, 35, 41, 55, 56,
Jaccard index (JI), 303
247, 249, 286, 290, 356
Matthew’s correlation coeﬃcient (MCC),
Karush-Kuhn-Tucker (KKT) conditions,
301
256, 349
maximal frequent pattern mining, 228,
kernel K-means clustering, 101, 103, 246,
235
360
maximum likelihood estimation, 25, 59,
kernel canonical correlation analysis
60, 128, 155, 157, 159, 351(KCCA), 269
mean, 18, 60, 324
kernel function, 89, 100, 103, 109, 269,
mean average precision (MAP), 319
337
mean reciprocal rank (MRR), 319
kernel learning, 89, 100, 109, 135, 145,
metabolic network, 179
169, 211, 229, 234, 239, 274
method of Lagrange multipliers, 22, 37,
kernel principal component analysis (ker86, 88, 95, 102, 104, 242, 249,
nel PCA), 107
266, 271, 348
kernel ridge regression, 103, 360
minimum cut, 243kernel trick, 100
minimum description length (MDL), 46
Kullback-Leibler (KL) divergence, 331minimum DFS code, 222, 226, 231
label propagation, 250, 258, 260, 281, mixture Markov model, 153, 154, 179
mixture pair model, 153
287, 290

INDEX374

molecular graph, 6, 11, 219, 235, 272
positive semidefinite
most likely path, 162, 185, 196
matrix, 56, 109, 241, 245, 334,
mostlikely path, 204335
multinomial distribution, 131posterior distribution, 25, 131
multinomial distribution, 130, 327
precision-recall (PR) curve, 309
multiple kernel learning, 274, 290
PrefixSpan algorithm, 147, 151
multiple kernel learning (MKL), 264
principal component analysis (PCA), 92,multiple sequence alignment (MSA), 180,94, 107, 110, 246, 265, 270, 351
183, 184, 186prior distribution, 25
multiplicative update, 38, 39
prior knowledge, 21, 64, 109, 130, 153,
multivariate analysis, 58
184, 186, 231, 274
multiview learning, 13, 262
probabilistic latent semantic analysis
mutual information, 330(pLSA), 127
probabilistic model, 23, 33
naive Bayes classifier, 51, 60, 72, 169
probability density function (PDF), 323,
natural language processing, 8, 12, 119,327
126, 152, 156, 163
probability mass function (PMF), 323,Needleman-Wunsch algorithm, 182
326
network sciences, 237
profile hidden Markov model
node degree, 11(profile HMM), 184, 185
nonparametric distribution, 138
pruning, 48
nonsingular matrix, 36, 37, 54, 56, 97, pyramid match kernel, 138332
normal distribution, 26, 60
rand index (RI), 313
normalized cut, 244
random forest, 75
normalized discounted cumulative gain ratio cut, 244(nDCG), 321
Rayleigh quotient, 96
normalized graph Laplacian, 244, 252, Rayleigh quotient, 23, 41, 103, 108, 245,260
253, 286, 350
normalized mutual information (NMI), Receiver Operator Characteristic (ROC)
315
curve, 305
recommendation, 41
observable variable, 3
recursive partitioning, 47, 74
ordered tree Markov model, 213
regression, 42
ordered tree Markov model (OTMM), regular matrix, see nonsingular matrix
198, 207
regularization, 42, 54
outlier, 5, 21
regularization coeﬃcient, 5, 37
overfitting, 42, 55, 72, 99, 129, 130
regularization term, see regularizer
regularizer, 4, 36, 55, 130
pair model, 153
relative risk, 302
pairwise sequence alignment (PSA), 180,
reverse search, 225, 226
184
ridge regression, 54, 56, 103, 105partitional clustering, 16, 31
root mean square deviation (RMSD),
pattern growth, 122, 146
312
perceptron, 57
positive semidefinite matrix, 141
sampling with replacement, 73, 75, 323
positive definite matrix, 37

INDEX
sampling without replacement, 323
tree, 6, 9, 12, 191
scalability, 109
tree grammar, 166, 187
semi-structured data, 12
tri-factorization, 35
semi-supervised classification, 280
unsupervised learning, 4, 16, 26
semi-supervised clustering, 280, 285
user-item matrix, 41
semi-supervised learning, 238, 279
sequence, 6, 8, 12, 115, 145variance, 60, 94, 324, 361
sequence classification, 118
vector, 5, 7, 15
set, 6, 7, 12
visualization, 32
sigmoid function, 57, 65, 69, 325
single nucleotide polymorphisms (SNPs), Viterbi algorithm, 163
116
weak learner, 84
singular matrix, 332
world wide web (WWW), 237
singular value decomposition (SVD), 36,96, 334
Z-test, 304
social network, 11
social network services (SNS), 237, 261,279
soft margin, 87
sparse learning, 37, 92, 98, 109, 239
spectral clustering, 103, 245, 248, 286,287, 290, 351
spectral embedding, see Laplacian eigenmaps
spectrum kernel, 170
speech recognition, 8, 12, 156, 163
squared error (loss), see L2 norm
standard deviation, 324
state transition diagram, 153, 155, 156,193
steepest descent, 347
step function, 57
string, 8
string kernel, 169, 190
subgraph, 11, 112, 220, 231
subsequence, 8, 112, 146
substring, 8
subtree, 10, 211, 213, 231
suﬃx tree, 172
supervised learning, 4, 26, 42
support vector machine, 359
support vector machine (SVM), 84, 100,103, 291
topic model, 127
traceback, 182, 185

375