Ba. Chapter 7

Front 1

No guarantee that there are meaningful pattern

Back 1

UNSUPERVISED LEARNING

Front 2

6 association rule mining

Back 2

Item set

Support count

Support

Frequent itemset

Association rule

Confidence

Front 3

find rules that will predict the occurence of an item

Back 3

ASSOCIATION RULE MINING

Front 4

support is greater or equal to minimum support

Back 4

FREQUENT ITEMSET

Front 5

finding hidden pattern within data

Back 5

UNSUPERVISED LEARNING

Front 6

no easy way to measure errors

Back 6

UNSUPERVISED LEARNING

Front 7

implication expression

Back 7

ASSOCIATION RULE

Front 8

find occurence of 1 item in relation to 1 item

Back 8

ASSOCIATION RULE MINING

Front 9

group items together that has the same characteristic.

Back 9

CLUSTERING

Front 10

collection of one or more item

Back 10

ITEM SET

Front 11

Finding groups of objects in a group will be similat to one another

Back 11

CLUSTERING

Front 12

ask data from the web

Back 12

WEB MINING

Front 13

may minimum # na gusto ireach

Back 13

FREQUENT ITEMSET

Front 14

Frequency on occurence af an itemset

Back 14

SUPPORT COUNT

Front 15

measure how often item lumabas

Back 15

CONFIDENCE

Front 16

reduce the size of large data sets

Back 16

SUMMARIZATION

Front 17

fraction of transactions contain an itemset

Back 17

SUPPORT

Front 18

groupings as a result of an extrnal specification

Back 18

RESULT OF A QUERY

Front 19

areas are not identical

Back 19

GRAPH PARTITIONING

Front 20

set of nested cluster

Back 20

HIERARCHICAL CLUST

Front 21

What is not cluster analysis

Back 21

SUPERVISED CLASSIFICATION

SIMPLE SEGMENTATION

RESULT OF A QUERY

GRAPH PARTITIONING

Front 22

have a class label information

Back 22

SUPERVISED CLASSIFICATION

Front 23

non overlapping subsets

Back 23

PARTITIONAL CLUSTERING

Front 24

tree like diagram that records the sequence of merge

Back 24

DENDOGRAM

Front 25

related document for browsing

Back 25

UNDERSTANDING

Front 26

each cluster associated with centroid

Back 26

K MEANS CLUSTERING

Front 27

visualized as dendogram

Back 27

HIERARCHICAL CLUSTERING

Front 28

most popular hierarchical technique

Back 28

AGGLOMERATIVE CLUSTERING ALGORITH

Front 29

mean of the point in the cluster

Back 29

CENTROID

Front 30

measured by euclidean dustance

Back 30

CLOSENESS

Front 31

CODE FOR PLOTING

Back 31

library(cluster)

Front 32

filtered out

Back 32

STOP WORDS

Front 33

basic algorithm

Back 33

STRAIGHT FORWARD

Front 34

data colection via web crawlers

Back 34

WEB CONTENT OR STRUCTURE MINING

Front 35

HTML, XML ,text format

Back 35

WEB MINING

Front 36

related to data mining and text

Back 36

WEB MINING

Front 37

pre processing

post processing

web content mining

search engine mining

Back 37

WEB CONTENT MINING

Front 38

textual content on the web

Back 38

WEB CONTENT OR STRUCTURE MINING

Front 39

generate structure summary

Back 39

WEB STRUCTURE MINING

Front 40

largest repository data

Back 40

WEB MINING

Front 41

hindi lang text pwedeng image, videos etc.

Back 41

WEB DATA

Front 42

semi-automated process

Back 42

TEXT MINING

Front 43

1st impost structure to the data then mine the structure data

Back 43

TEXT MINING

Front 44

reducing inflected words

Back 44

STEMMING

Front 45

large collection of structure texts

Back 45

CORPHA (CORPUS)

Front 46

nakaarrange accdg. to table

Back 46

STRUCTURED DATA

Front 47

categorized block of text

Back 47

TOKENING

Front 48

Determing the lifetime value of clients

Back 48

WEB MINING

Front 49

sequenrial patterns

Back 49

DATA MINING TECHNIQUES

Front 50

loses all order specific

Back 50

BAG OF TOKEN APPROACH

Front 51

single word or phrase

Back 51

TERM