Data Preprocessing: Discretization and Imputation

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Data Preprocessing: Discretization and Imputation
A review of recent research output
Md Zahidul Islam, Md Geaur Rahman & Michael Furner
Charles Sturt University: School of Computing and Mathematics
Introduction
FIMUS
Organizations use Data Mining and Knowledge Discovery algorithms for making better decisions. A
data mining algorithm extracts interesting patterns (such as logic rules and clusters) that could otherwise be extremely difficult for us to extract [15, 7]. Data preprocessing and cleansing play a vital role
in data mining by ensuring good quality of data. Data-cleansing tasks include imputation of missing values, identification of outliers, and identification and correction of noisy data [5]. Another key
preprocessing technique is discretization - the conversion of numerical attributes into categorical attributes [11]. This can be performed to allow the use of data mining techniques that require categorical
attributes, increase the performance of data-mining techniques, and to convert a numerical attribute to
categorical to be used in a classifier.
The data mining research group has published several papers exploring these concepts. This has
lead to the development of a variety of new algorithms, including LFD [11] for discretization, and
FIMUS [10], SiMI [9], FEMI [12] and MultiSiMI [2] for missing value imputation. This poster will
discuss and provide a brief overview of these two topics, and provide a brief overview of each of the
algorithms developed by the data mining research group.
In order
√ to discretize an attribute, FIMUS splits the attribute into
Nc = s categories where s = max(Ai) − min(Ai) + 1. This allows
FIMUS to automatically determine the number of categories, a feature
not found in some discretization techniques. The algorithm then divides the values into Nc categories, each containing values in intervals
of the size Ns . Due to this process there may be empty categories, but
c
these are ignored. Figure 1 shows discretization on a toy dataset performed by FIMUS’ technique. This technique was shown to perform
better than the PD [18] and FFD [18] discretization algorithms for the
purposes of imputation using FIMUS [10].
Contact Information:
School of Computing and Mathematics
Charles Sturt University
Panorama Avenue, Bathurst, NSW
Phone: (02) 6338 4214
Email: [email protected]
SiMI [9] improved upon DMI’s imputation accuracy even further, by
replacing the role of a decision tree with a decision forest. In order
to get very high correlation and similarity subsets which are mutually
exclusive, SiMI finds the intersections between leaves from the different trees of a decision forest. This concept is illustrated in figure
2. SiMI [9] was expanded once again with the development of MultiSiMI [2], which instead of finding intersections to get mutually exclusive subsets of records simply performs EMI on every subset found
by the decision trees in the forest. This gives n imputations for each
record, where n is the number of trees in the forest. The results are
then averaged - part of a concept known as multiple imputation [13].
3. Generate co-appearance matrix (C), normalized similarity matrix Sj
for attribute Aj ; ∀Aj ∈ A, and correlation matrix K.
4. Impute missing values.
5. Repeat the imputation process (steps 2-4) until there is a change between two consecutive iterations.
6. Return a completed data set (Do) without any missing values.
The similarity matrix describes the similarity between different values in the same attribute, allowing a measure of “closeness” to be used
in the imputation calculations [4]. FIMUS works on one attribute at
a time, imputing by considering two votes for each missing value for
each non-missing attribute in the same record. These are
FEMI
Discretization
Many data mining algorithms, for example Naive Bayes, can only
deal with categorical attributes and are unable to handle numerical attributes [18]. Some other data mining algorithms can handle numerical attributes. However, often the efficiency and effectiveness of a data
mining algorithm increases when it makes use of a discretization algorithm [3, 18]. Discretization is also considered to be an important part
of data preprocessing and cleansing that is likely to improve the quality
of the results obtained by various data mining algorithms [5].
The LFD algorithm [11] has been developed for the express purpose
of discretization. The FIMUS missing value imputation algorithm [10]
includes a novel discretization technique, which will also be discussed
here separately to its imputation context.
LFD
The Low Frequency Discretizer algorithm [11] is designed to target a
fundamental flaw in many existing discretization techniques, namely
that choosing an interval boundary that is in a region of the attribute
space that has many occurances in the dataset causes a large amount
of information about the similarity between ”close” values to be lost.
In order to combat this, LFD automatically selects splitting points between categories that are in Low Frequency regions of the attribute
space.
This process takes four steps:
0 .
1. Copy a full dataset DF into DF
0 based on correlation ratio η.
2. Rank the numerical attributes of DF
0 .
3. Discretize all numerical attributes of DF
0 .
4. Return the discretized dataset DF
The numerical attributes are ranked since the discretization process
happens one attribute at a time in order of ranking. Since the discretization process relies on the other attributes, it is crucial that the initial discretizations are of high quality. The attributes are discretized by finding
high quality cut points automatically. The cut points need to be between attribute values with lower than average frequencies. The different possible cut points for an attribute are considered and the potential
categories are voted on by considering the attribute-interdependency
and uncertainty between the potential categories and the other categorical attributes in the dataset. The best splitting point is selected from
the votes. This process is repeated until the vote no longer improves
on a new iteration. This is then repeated for the next ranked numerical
attribute.
LFD was shown to improve the quality of imputation techniques,
classification techniques and noise detection techniques over the results obtained by other discretization algorithms including PD, FFD,
EWD, EFD, and CAIM [18, 6, 17, 11].
Figure 1: FIMUS discretization
Missing Value Imputation
Natural data sets often have missing values in them. The imputation
of missing values as accurately as possible is an important data preprocessing task. Use of poor-quality data, having missing and incorrect values, can result in an inaccurate and non-sensible conclusion,
making the whole process of data collection and analysis useless for
the users [5, 8].
DMI [9], SiMI [9], MultiSiMI [2] are all imputation techniques that
rely on EMI [14] and decision trees. FEMI [12] uses a fuzzy version
of EMI and fuzzy c-means clustering in order to find an imputation.
FIMUS [10] uses co-appearance, correlation and similarity analysis
for imputations.
DMI, SiMI and MultiSiMI
A decision tree divides a data set into a number of leaves having sets
of mutually exclusive records. A decision forest builds a number of
decision trees. While these sets of records are usually used to classify
records as part of a classification problem, they have been shown to
contain sets of highly correlated and similar records [9]. The Expectation Maximisation Imputation algorithm (EMI) [14] relies on correlation and similarity, and is more effective when the records it is used on
are highly correlated and similar.
DMI [9] was designed to make use of the sets created by decision
trees to increase imputation accuracy. It does this by building a decision tree on the clean records of a dataset, and assigning the missing
value records to a leaf. EMI is then performed on each of the subsets
separately to impute numerical attributes, and categorical attributes use
a mode imputation within the subset. This was shown to be a marked
improvement over EMI [14], as well as several other imputation algorithms [9].
Figure 2: SiMI’s tree intersections
FEMI [12] is an advanced technique that uses a fuzzified version of
EMI known as FuzzyEM in order to use the fuzzy c-means clustering
algorithm to find subsets of records. The process consists of 6 steps:
1. Copy a full data set DF into DN and normalize all numerical attributes of DN within a range between 0 and 1.
2. Divide the data set DN into two sub data sets DC (having only
records without missing values) and DI (having only records with
missing values).
3. Find membership degrees of all records of DC and DI with all clusters.
4. Apply the FuzzyEM method to impute numerical missing values using all clusters.
5. Find the combined imputed value of a numerical attribute. Find the
imputed value of a categorical attribute.
0 ) without any
6. Combine records to form a completed data set (DF
missing values.
In a fuzzy clustering algorithm, every record has a degree of membership to every cluster. FuzzyEM [12] is used on each cluster, and makes
use of the membership degrees of each record to impute using means
and covariance specific to each cluster (using membership degrees as
weights) for each cluster. This results in c imputations (where c is the
number of clusters). These are combined, again using membership degrees, to find a single imputation result. Categorical attributes are also
imputed using the membership degrees from the clustering process in
order to affect where the imputation comes from.
Cxl
N.p
Vx =
fl
X C
S.p
xl
× Sla
Vx =
f
∀a∈Ap l
Where Cxl is the is the number of coappearances between the value l
in non-missing attribute Ap and a candidate value x, fl is the frequency
of l in the whole dataset for Ap, and Sla is the similarity between values
p
N.p
S.p
l and a in Ap. These are combined as Vx = {Vx × λ + Vx × (1 −
λ)} × kjp. This is the vote in favour of the value x for the missing attribute considering attribute p. This is repeated for each attribute value
in the missing attribute and each other available attribute in the record.
Finally, the vote for
a particular value for the missing attribute is exP
p
pressed as Vxt = ∀Ap∈A Aj Vx . The attribute value with the highest
vote will be the result of the imputation.
Because the algorithm works on a discretized dataset, missing numerical attributes are initially imputed into a discrete category, and then
FIMUS is repeated on a subset of the dataset found by selecting all
records with the same category in the imputed attribute and replacing
all of the generalised values with their original values in the imputed
attribute. Each actual numerical value is then used as a category for a
repeat of FIMUS which gives the numerical imputation. FIMUS was
shown to provide significantly better results than DMI [12], SVR [16],
EMI [14] and IBLLS [1].
References
[1] Kin-On Cheng, Ngai-Fong Law, and Wan-Chi Siu. Iterative bicluster-based least square framework for estimation of missing values in
microarray gene expression data. Pattern recognition, 45(4):1281–1289, 2012.
[2] Michael Furner and Md Zahidul Islam. Multiple imputation on partitioned datasets. In Proceedings of the 13th Australasian Data Mining
Conference. Australian Computer Society, Inc., 2015.
[3] Sergio Garcia, Julián Luengo, José Antonio Sáez, Victor Lopez, and Francisco Herrera. A survey of discretization techniques: taxonomy and
empirical analysis in supervised learning. Knowledge and Data Engineering, IEEE Transactions on, 25(4):734–750, 2013.
[4] Helen Giggins and Ljiljana Brankovic. Vicus: a noise addition technique for categorical data. In Proceedings of the Tenth Australasian Data
Mining Conference-Volume 134, pages 139–148. Australian Computer Society, Inc., 2012.
[5] Jiawei Han, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques: concepts and techniques. Elsevier, 2011.
[6] Lukasz Kurgan, Krzysztof J Cios, et al. Caim discretization algorithm. Knowledge and Data Engineering, IEEE Transactions on, 16(2):145–
153, 2004.
Figure 3: Block diagram of FEMI’s process.
[7] Dorian Pyle. Data preparation for data mining, volume 1. Morgan Kaufmann, 1999.
[8] Md Geaur Rahman and Md Zahidul Islam. Data quality improvement by imputation of missing values. In International conference on
computer science and information technology (CSIT-2013). Yogyakarta, Indonesia, pages 82–88, 2013.
Figure 3 provides an overview of the technique. Like the other algorithms, FEMI has shown to significantly improve the results of older,
more traditional methods of missing value imputation [12].
FIMUS
FIMUS [10] is an algorithm for imputing missing values that uses a
combination of similarity, correlation, and co-appearance of values in
records to determine a vote for the imputed value.
It does this as follows:
1. Initialize a missing matrix B from the input data set Do.
2. Generalize all numerical attributes of Do.
[9] Md Geaur Rahman and Md Zahidul Islam. Missing value imputation using decision trees and decision forests by splitting and merging
records: two novel techniques. Knowledge-Based Systems, 53:51–65, 2013.
[10] Md Geaur Rahman and Md Zahidul Islam. Fimus: A framework for imputing missing values using co-appearance, correlation and similarity
analysis. Knowledge-Based Systems, 56:311–327, 2014.
[11] Md Geaur Rahman and Md Zahidul Islam. Discretization of continuous attributes through low frequency numerical values and attribute
interdependency. Expert Systems with Applications, 2015.
[12] Md Geaur Rahman and Md Zahidul Islam. Missing value imputation using a fuzzy clustering-based em approach. Knowledge and Information
Systems, pages 1–34, 2015.
[13] Joseph L Schafer and John W Graham. Missing data: our view of the state of the art. Psychological methods, 7(2):147, 2002.
[14] Tapio Schneider. Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values.
Journal of Climate, 14(5):853–871, 2001.
[15] Jason D Van Hulse, Taghi M Khoshgoftaar, and Haiying Huang. The pairwise attribute noise detection algorithm. Knowledge and Information
Systems, 11(2):171–190, 2007.
[16] Xian Wang, Ao Li, Zhaohui Jiang, and Huanqing Feng. Missing value estimation for dna microarray gene expression data by support vector
regression imputation and orthogonal coding scheme. BMC bioinformatics, 7(1):32, 2006.
[17] Andrew KC Wong and David KY Chiu. Synthesizing statistical knowledge from incomplete mixed-mode data. Pattern Analysis and Machine
Intelligence, IEEE Transactions on, (6):796–805, 1987.
[18] Ying Yang and Geoffrey I Webb. Discretization for naive-bayes learning: managing discretization bias and variance. Machine learning,
74(1):39–74, 2009.
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