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• Classic-Fiction

cloud computing31

cluster analysis19–20, 443–495

advanced497–541

agglomerative hierarchical clustering459–461

applications444, 490

attribute types and446

as automatic classification445

biclustering511, 512–519

BIRCH458, 462–466

Chameleon458, 466–467

CLIQUE481–483

clustering quality measurement484, 487–490

clustering tendency assessment484–486

constraint-based447, 497, 532–538

correlation-based511

as data redundancy technique108

as data segmentation445

DBSCAN471–473

DENCLUE476–479

density-based methods449, 471–479, 491

in derived space519–520

dimensionality reduction methods519–522

discretization by116

distance measures461–462

distance-based445

divisive hierarchical clustering459–461

evaluation483–490, 491

example20

expectation-maximization (EM) algorithm505–508

graph and network data497, 522–532

grid-based methods450, 479–483, 491

heterogeneous networks593

hierarchical methods449, 457–470, 491

high-dimensional data447, 497, 508–522

homogeneous networks593

in image recognition444

incremental446

interpretability447

k-means451–454

k-medoids454–457

k-modes454

in large databases445

as learning by observation445

low-dimensional509

methods448–451

multiple-phase458–459

number of clusters determination484, 486–487

OPTICS473–476

orthogonal aspects491

for outlier detection445

outlier detection and543

partitioning methods448, 451–457, 491

pattern282, 308–310

probabilistic hierarchical clustering467–470

probability model-based497–508

PROCLUS511

requirements445–448, 490–491

scalability446

in search results organization444

spatial595

spectral519–522

as standalone tool445

STING479–481

subspace318–319, 448

subspace search methods510–511

taxonomy formation20

techniques443, 444

as unsupervised learning445

usability447

use of444

cluster computing31

cluster samples108–109

cluster-based local outlier factor (CBLOF)569–570

clustering. seecluster analysis

clustering features462, 463, 464

Clustering Large Applications based upon Randomized Search (CLARANS)457

Clustering Large Applications (CLARA)456–457

clustering quality measurement, 484t487–490

cluster completeness488

cluster homogeneity487–488

extrinsic methods487–489

intrinsic methods487, 489–490

rag bag488

silhouette coefficient489–490

small cluster preservation488

clustering space448

clustering tendency assessment484–486

homogeneous hypothesis486

Hopkins statistic484–485

nonhomogeneous hypothesis486

nonuniform distribution of data484 see alsocluster analysis

clustering with obstacles problem537

clustering-based methods552, 567–571

example553 see alsooutlier detection

clustering-based outlier detection567–571, 582

approaches567

distance to closest cluster568–569

fixed-width clustering570

intrusion detection by569–570

objects not belonging to a cluster568

in small clusters570–571

weakness of571

clustering-based quantitative associations290–291

clusters66, 443, 444, 490

arbitrary shape, discovery of446

assignment rule497–498

completeness488

constraints on533

cuts and529–530

density-based472

determining number of484, 486–487

discovery of318

fuzzy499–501

graph clusters, finding528–529

on high-dimensional data509

homogeneity487–488

merging469, 470

ordering474–475, 477

pattern-based516

probabilistic502–503

separation of447

shapes471

small, preservation488

CMAR. seeClassification based on Multiple Association Rules

CN2359, 363

collaborative recommender systems610, 617, 618

collective outlier detection548, 582

categories of576

contextual outlier detection versus575

on graph data576

structure discovery575

collective outliers575, 581

mining575–576

co-location patterns319, 595

colossal patterns302, 320

core descendants305, 306

core patterns304–305

illustrated303

mining challenge302–303

Pattern-Fusion mining302–307