# Data Mining and KD at TU München

## Flashcards and summaries for Data Mining and KD at the TU München

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Scales for numerical measurements

### Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

How do you represent data  when there is no feature vector for the objects

### Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

What does Fourier analysis allow us?

Fuzzy histogram

Histogram

Sammon mapping

### Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

MDS - what is it?

### Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

Why do we need data transformation?

### Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

Exponential ﬁlter

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Asymmetric windows

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Symmetric filtering

### Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

Inlier - how to detect?

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## Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

Data Mining and KD

Scales for numerical measurements

Ratio

= > < + –  * /

21 years, 273 Kelvin

Generalized mean

Interval

= > < + –
2015 A.D, 20 C

Mean

Ordinal

> < =

A,B,C,D,F

Median

Nominal

Alice, Bob, Carol

Mode

Data Mining and KD

How do you represent data  when there is no feature vector for the objects

the relation of all pairs of objects can often be quantiﬁed and written as a square matrix

Each relation may refer to a degree of:

• similarity, dissimilarity,
• compatibility, incompatibility,
• proximity or distance

Data Mining and KD

What does Fourier analysis allow us?

Given time series data, Fourier analysis allows us to compute the

• amplitude spectrum Y (y1….ym) and the
• phase spectrum P (p1….pm)

that represent the:

frequencies, amplitudes, and phase angles of the spectral components of the time series.

Data Mining and KD

Fuzzy histogram

• partially counts data for several neighboring bins
• For example, a number at the border between two bins may be counted as half for one and a half for the other bin

Data Mining and KD

Histogram

• Equally sized intervals
• The left and right borders of each bar represent the lower and upper limits of the corresponding data interval.
• The height of each bar

represents the interval count.

Data Mining and KD

Sammon mapping

Idea: map a dataset X to a data set Y

like MDS,

it simply provides a measure of how well the result of a transformation reflects the structure present in the original dataset, in the sense described above.

In other words, we are attempting not to find an optimal mapping to apply to the original data, but rather to construct a new lower-dimensional dataset, which has structure as similar to the first dataset as possible.

Data Mining and KD

MDS - what is it?

MDS of a feature data set X yields the same results as PCA.

More than PCA:

produce an (approximate) feature space representation Y for relational data speciﬁed
by a Euclidean distance matrix D.

Data Mining and KD

Why do we need data transformation?

• incorrect results may be obtained IF the ranges of the feature are so different
• Also the choice of the feature units might be arbitrary.

Data Mining and KD

Exponential ﬁlter

• The exponential ﬁlter works best with slow changes of the ﬁltered data
• for time series
• The current ﬁlter output is affected by each past ﬁlter output  with the multiplier so the ﬁlter exponentially forgets previous ﬁlter outputs, hence the name exponential ﬁlter.
• The outlier suppression is much
weaker than with the median ﬁlter

Data Mining and KD

Asymmetric windows

• Even order
• Asymmetric windows are also suitable for online ﬁltering and are able to provide each ﬁlter output yk as soon as xk is known.

Data Mining and KD

Symmetric filtering

odd order q=(3; 5; 7…..)

Symmetric windows are only suitable for ofﬂine ﬁltering when the future values of the series are already known

Data Mining and KD

Inlier - how to detect?

• They are local outliers
• Can be  detected when considering the differential change of subsequent values

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