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DiscoVars: A New Data Analysis Perspective -Application in Variable Selection for Clustering

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We present a new data analysis perspective to determine variable importance regardless of the underlying learning task. Traditionally, variable selection is considered an important step in supervised learning for both classification and regression problems. The variable selection also becomes critical when costs associated with the data collection and storage are considerably high for cases like remote sensing. Therefore, we propose a new methodology to select important variables from the data by first creating dependency networks among all variables and then ranking them (i.e. nodes) by graph centrality measures. Selecting Top-n variables according to preferred centrality measure will yield a strong candidate subset of variables for further learning tasks e.g. clustering. We present our tool as a Shiny app which is a user-friendly interface development environment. We also extend the user interface for two well-known unsupervised variable selection methods from literature for comparison reasons.
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DiscoVars: A New Data Analysis
Perspective - Application in Variable
Selection for Clustering
by Ayhan Demiriz
Abstract We present a new data analysis perspective to determine variable importance regardless
of the underlying learning task. Traditionally, variable selection is considered an important step
in supervised learning for both classification and regression problems. The variable selection also
becomes critical when costs associated with the data collection and storage are considerably high for
cases like remote sensing. Therefore, we propose a new methodology to select important variables
from the data by first creating dependency networks among all variables and then ranking them
(i.e. nodes) by graph centrality measures. Selecting Top-
variables according to preferred centrality
measure will yield a strong candidate subset of variables for further learning tasks e.g. clustering. We
present our tool as a Shiny app which is a user-friendly interface development environment. We also
extend the user interface for two well-known unsupervised variable selection methods from literature
for comparison reasons.
Applications of machine learning and related technologies are dependent on successful implemen-
tations of learning algorithms and strong data analytic skills of practitioners. Often complexity and
black box nature of the algorithms disengage practitioners from the knowledge discovery process.
Recently, high dimensional data and uninterpretable complex models are two major reasons that
practitioners have started shying away from interacting with learning models and have preferred
AutoML Martinez-Plumed et al. (2021); Truong et al. (2019). Incorporating domain knowledge and
expertise into algorithmic models certainly allows practitioners to manage the learning process.
Feature (variable) selection is an important task and can be performed by employing a large
array of methods in the literature Li et al. (2018); Guyon and Elisseeff (2003); Caruana and de Sa
(2003); Forman (2003). Depending on learning task, supervised and unsupervised variable selection
are possible. Considering that machine learning problems have been studied for several decades,
many well known methods were originally developed under very limited computational resource
constraints. Naturally, dimension reduction is one way of reducing problem complexity for the
underlying methods. Feature extraction and feature selection are two alternative choices for dimension
reduction Li et al. (2018). The first alternative is typically used for projecting the full input space to a
lower dimension. Feature extraction requires the usage of full input data in data preparation stage
of the underlying learning method. Moreover, such methods may still require more computational
resources and full data at later stages of the learning process. Feature selection, on the other hand,
picks a subset of all variables presumably without any sacrifice in the performance of learning process.
The purpose of feature selection is primarily to reduce the computational complexity and then to
increase the performance of learning process which could be in supervised, unsupervised and semi-
supervised fashions. Overfitting is also another reason to utilize feature selection to prevent very poor
generalization in learning processes.
Categorization of the feature selection methods is done from various perspectives in practice
Li et al. (2018); Guyon and Elisseeff (2003). Supervision and selection strategy perspectives are the
most common categorization of feature selection methods Li et al. (2018). Depending on level of
label data, supervised, unsupervised and semi-supervised approaches are utilized in feature selection.
Specifically, label information is used in classification and regression problems. Subset of features
are selected based on some performance measures such as accuracy and
. In addition to level of
supervision, selection strategy is another categorization of feature selection methods. Selection could
be implemented as a wrapper method which calls the learning model like a black box with different
combinations of input data and searches for the best possible subset of variables. Since there are 2
different input possibilities for
-dimensional data, the combinatoric nature of this approach may
slow down the search. However, introducing some evaluation criteria may speed up the process.
For example, traditional stepwise, backward and forward selection methods can be considered as
wrapper methods in multivariate regression problems and these methods simply pick a feature based
on its contribution to the overall
value at each iteration. Usually one feature is added/subtracted
from the regression model at each iterative search step. Obviously, supervision is also used. The
second approach for selection strategy is filter methods. The main idea is to select a subset of variables
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by ranking them prior to running the learning algorithm. Potentially, filter methods can be used
irrespective of underlying learning task i.e. supervised, unsupervised or semi-supervised. Ranking
could be based on an intrinsic measure such as correlation and mutual information Chandrashekar
and Sahin (2014). Filter methods are composed of two steps: the first step ranks the variables according
to some measure either univariate or multivariate (i.e. multiple features) way, the second step removes
low ranked features Li et al. (2018). The third way of selection strategy is to embed feature selection
into model learning Li et al. (2018). This is somewhat a hybrid approach by combining best parts of
previous two approaches.
We propose an interactive multivariate filter method that first creates a dependency network
Heckerman et al. (2000) among all features then the nodes of this network are ranked based on
centrality measure chosen by the user. For commonly used network centrality measures, interested
user is referred to Hansen et al. (2020). Finally, Top-
variables are selected by the user (modeler). Our
approach enables modeler to discover candidate variable subset through an interactive method by
utilizing dependency networks and graph centrality measures. Note that each node in dependency
networks corresponds to a variable. Thus our method is named as DiscoVars (Discover Variables).
Since supervised feature selection methods are established on concrete performance measures, we opt
to implement our feature selection method for clustering analysis. Nevertheless, our approach can
directly be used in classification problems as well. Novelty of our approach is to utilize graph centrality
metrics to determine importance of variables on dependency networks constructed by efficient and
proven variable selection methods such as stepwise, forward, and Lasso Tibshirani (1996).
Throughout the paper,
dimensional dataset,
-th dimension of
is practically
the random variable
, Top-
variables are the selected
features by the user (modeler),
dimensionally reduced dataset, and
is the number of clusters set for clustering algorithms, a
dependency network is a directed graph
G= (V
of vertices
, edges
. The organization of the
paper is as follows. Section 2introduces the methodology proposed in this paper. As an application
domain, unsupervised feature selection is considered suitable for our methodology. Section 3reviews
some related work in feature selection for clustering problems. Comparison of performances of
clustering methods is very subjective by its nature. We report the detail implementation of our
proposed approach DiscoVars in Section 4. For comparison reasons we also implemented two-well
known feature selection methods on Shiny framework in Section 5. Section 6then concludes our
Graphical models Koller et al. (2007) are considered as a popular tool for represent real world problems
by combining uncertainty and logical structure i.e. conditional independence of variables. Bayesian
and Markov Networks are the most common graphical models studied in the literature. Bayesian
Networks are directed graphs. Markov Networks, on the other hand are undirected graphs. Graphical
models primarily help on probabilistic inference. Thus, the goal is to derive a joint distribution
over set of random variables
. . .
. Conditional independence is an important structural
concept that simplifies underlying graphical models. According to Definition 2.1 of Koller et al. (2007),
conditional independence is defined as
Y=y|Z=z) = P(X=x|Z=z)P(Y=y|Z=z)
for all
, and
values. Assuming conditional independence of variables
. . .
in a Bayesian
Network (BN) G, one can factorize joint probability distribution of BN as,
PB(X1, . . . , Xd) =
are the parents of random variable
. Note that this rule is also called as chain rule of
BN. A greedy algorithm that combines local and global structure search is proposed in Chickering
et al. (1997) which utilizes conditional probability distributions. We implicitly assume that a random
and its non-descendants are conditionally independent given
’s parents
. Markov
Networks can also be constructed by factorizing over structural sub-graphs that are independent.
Graphical models are computationally expensive to construct. In addition, it is very hard to interpret
these models by untrained practitioners. Modelers prefer to interpret connections in these networks as
relationships. However, relationships are casual and may not necessarily correspond to predictive
Dependency networks (DNs) are considered as computationally efficient to construct Heckerman
et al. (2000). The primary intention to develop DNs was to visualize predictive relationships. Commer-
cial version was available with MSSQL Server 2000. It is claimed in Heckerman et al. (2000) that it
was very useful to discover dependency relationships as a graphical tool. By varying (increasing) the
strength threshold of relationships, sparser networks can be drawn. The simplicity of a consistent DN
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comes from the fact that conditional distributions of variables Xican be found as:
P(Xi|Pai) = P(Xi|X\Xi),
means all the variables in
and DN is consistent in a way that local probability
distributions can be obtained from
. It is shown in Heckerman et al. (2000) that consistent DNs
are equivalent to Markov Networks. In other words, one can learn the structure of Markov Network
from a consistent DN. This is an important result that makes DNs as practical graphical models
for probabilistic inference, predicting preferences (collaborative filtering) Heckerman et al. (2000),
and sequential data inference (prediction) Carlson et al. (2008). Constructing a DN may require
using classification/regression models for estimating the local distributions Heckerman et al. (2000).
In commercial setting, probabilistic decision trees were implemented as a default learning method
because of their simplicity and computational efficiency. Note that DNs are primarily used for
supervised prediction problems. So, the underlying graph represents the significant relationships for
the target (label) variable in consideration.
Our approach is primarily based on constructing a DN by fitting each variable
with the
remaining variables
to represent all the significant relationships among all the variables. A
statistical variable selection method
should be used for determining significant variables for
local distributions. Assume that the set
represents the indices of significant variables for
and the
are parents
. In other words,
depends on variables
. All of these relationships
form the DN G. Note that we assume that all
variables are continuous variables for this paper.
Categorical data and classification methods can subsequently be incorporated into constructing DNs
easily. So Stepwise, Forward, Akaike Information Criterion (AIC) and Lasso selection methods can
be utilized by linear regression models (lm) in our approach at this time. The pseudo code of our
methodology is given in Algorithm 1.
Algorithm 1 DiscoVars: Construct DN G
1: Given Xand M
2: for i1, ddo
3: Fit Xilm(X\Xi,M)
4: s← {si gni f ica nt var iabl es}
5: Eis 1
6: end for
7: cSelect Centrality Measure
8: Rank(G,c,X)
9: Select Top n variables
Interactive nature of our approach is based on choices of variable selection method
, centrality
for Top-
important variables. The methodology given in Algorithm 1is parallelizable
due to the nature of
loop. However, one can argue that it is computationally expensive to construct
regression models in the first place. Note that we can still apply filter methods to screen very high
dimensional data ahead of constructing DNs if there is a consideration of computational resources. A
stochastic search algorithm is proposed for constructing DNs on gene expression data for the purpose
of supervised variable selection in Dobra (2009). Underlying graph structure among features can
improve classification models for genome data Sun et al. (2020). By coupling graph structure with
feature selection performs better than traditional feature selection methods Sun et al. (2020).
Centrality measures are used in our approach to rank nodes (variables) in DNs. It is a well
established research topic to quantify structural importance of actors in a network Borgatti (2006). The
centrality measures suitable for directed graphs such as betweenness, closeness, degree, eigenvector,
and pagerank etc. can be used in our methodology. In common social network analysis problems,
networks are composed of objects, people, or events. But features (variables) are the center of attention
in our approach. In general, network science aims to study the structural relationships and importance
in networks. Network topology determines the structural importance of nodes Roddenberry and
Segarra (2020). Centrality measures are usually computed based on full network topology. Some recent
work enables inference of eigenvector rankings from data directly without inferring the full network
topology Roddenberry and Segarra (2020,2021). So one can argue that it is technically possible to
rank variables without first constructing DNs, but this is not the scope of this paper. The full network
topology is constructed first in this study to calculate centrality measures.
In Meinshausen and Bühlmann (2006), local neighborhood structures are found by utilizing Lasso
Tibshirani (1996). The main idea in Meinshausen and Bühlmann (2006) is to estimate the graph struc-
ture of covariance matrix for high dimensional data. Similar to our approach local dependencies are
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found by Lasso. Note that that Lasso is an option in our implementation. Our approach basically dif-
fers from Meinshausen and Bühlmann (2006) once DN is constructed. In Meinshausen and Bühlmann
(2006), Lasso is used for constructing a sparse graph structure that all remaining variables in the graph
become relevant for the learning task. Note that our approach is independent from learning task.
Centrality measures are further used to filter most important variables (nodes) in the graph. There
exist also distributional assumptions in Meinshausen and Bühlmann (2006). The size of neighborhood
in Lasso is very sensitive to the selection of regularization parameter
. Generally, cross-validation
is common way of setting the parameter. In extreme cases, very sparse and full connections may be
found by Lasso. Some remedies are offered in Meinshausen and Bühlmann (2006) for Lasso to result
in robust neighborhood formation i.e. connections in network. Therefore neighborhoods are formed
in a more stable way compared to original Lasso formulation. Same situation may also be an issue in
our approach. If network is extremely sparse or almost full in connections, graph centrality measures
will not yield decisive rankings.
Structural properties of variable neighborhoods can also help computing Laplacian Scores He et al.
(2005). Variables can be ranked based on this score. This is another filter method that shares similar
perspective with our work. Moreover, traveling salesman problem (TSP) is a universal test bed of
ideas. In regular TSP, nodes (cities) have full connections. In other words, one can travel from any city
to any other city directly. However, if sparse connections exist meaning that one can only move over
physical road networks, we can rank cities based on underlying graph (network) structure. Thus, TSP
can be solved by ranking cities first Demiriz (2009). This is also very similar to our idea in this paper.
Our approach can be used as a filter method irrespective of underlying learning task. There are
widely used and proven feature selection methods for supervised learning problems due to availability
of robust performance measures. We think that applying our approach may make an impact on
unsupervised feature selection. Therefore,
can easily be clustered by any clustering algorithm after
applying Algorithm 1.
Unsupervised feature selection
In practice, there exist some metrics to compare clustering results such as Davies-Bouldin Index (DBI)
Davies and Bouldin (1979) and Adjusted Rand Index (ARI) Hubert and Arabie (1985). It is technically
possible to devise a wrapper algorithm by incorporating some intelligence in search mechanism to
run clustering methods to come up with best possible feature subset. These clustering indices are easy
to interpret but they are not guaranteed to be useful for a robust search optimization. They are not
only dependent on features selected but also number of partitions at the same time. Certainly, some
heuristics methods can be deployed to find a suitable subset of features. However this is not within
the scope of our paper.
An excellent review of feature selection methods for model-based clustering is given in Fop and
Murphy (2018). Technically, the aim of variable selection is to determine the set of relevant variables
for clustering. Logically, the remaining variables are called irrelevant. Two major assumptions are local
conditional independence assumption of relevant variables within clusters and global independence
assumption of irrelevant and relevant variables. Model-based clustering assumes that each observation
comes from a finite mixture of
probability distributions. Obviously, each distribution represents a
different group Raftery and Dean (2006); Scrucca and Raftery (2018); Scrucca et al. (2016). Bayesian
Information Criterion (BIC) is commonly used in model (i.e. performance) comparison for clustering.
BIC can be calculated as follows Raftery and Dean (2006).
BI C =2log(maximized likelihood)(no. of parameters)log(m).
Technically, BIC values of inclusion and exclusion of variable
can be compared and a decision is
made regarding that variable Scrucca and Raftery (2018).
A Lasso like approach is proposed in Witten and Tibshirani (2010). Technically, between cluster
sum of squares for feature
is optimized and regularization terms
(Lasso) and
are applied on
weights of features.
penalty term guarantees non-zero solution.
penalty term forces for sparser
solutions Witten and Tibshirani (2010).
Witten and Tibshirani (2010) maximizes between cluster sum of squares. Alternatively, it is also
possible to reduce within group variance Andrews and McNicholas (2014). VSCC method Andrews
and McNicholas (2014) selects iteratively those variables that have smaller within-group variance
and are correlated. We believe that feature selection for model-based clustering is well-studied.
Interested readers are referred to Fop and Murphy (2018). In Section 5, two well-known methods are
implemented in Shiny framework for comparison and reproducibility purposes.
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Our approach is implemented by using Shiny
framework in R to design and develop interactive
applications (Figure 1). The current version of DiscoVars
can be accessed at Github repository
. As
outlined in Algorithm 1, DiscoVars consists of two main steps:
Constructing DN
Ranking and selecting Top-
variables interactively according to choice of network centrality
DiscoVars can import data from various sources by utilizing datamods
R package (see Figure 2).
datamods is also a Shiny application. As seen in Figure 2, user can import datasets available from
other R packages such as mlbench. Notice that
Dataset from mlbench is used for
illustration purposes.
Figure 1: DiscoVars Opening Screen
Note that only numeric variables are included in our implementation for the time being. Categori-
cal variables can also be included via probabilistic decision tree models to construct DNs. Once the
dataset selected by the user as in Figure 2, user can filter data and/or exclude some unrelated variables
(such as numerical ID variable) from dataset. Once dataset is imported, only numeric variables are
used in DiscoVars. Imported dataset is presented to the user via
object (Figure 3). At
this moment, user can run dependency discoverer by choice of variable selection method
. Four
well-known variable selection methods are available in DiscoVars. The default method is
are also presented to the user as alternatives.
methods are available from
is available from MASS package and
available from glmnet package in R. For
0.1 entry and
0.25 exit parameters
are set. For
0.1 entry parameter is set. Default parameter settings are used for
. With the default settings, glmnet runs Lasso with a varying number of
values. Therefore,
a model selection is required. The parameter
in glmnet is set to 16
is the number of
rows. Cross-validation can be used in glmnet to pick the best model. This option is not use to avoid
extended run times.
DN can be constructed by running
regression models as given in step 3 of Algorithm 1. Since this
step is fully parallelizable,
regression models are run by calling
function in doParallel
R package. Parallelization obviously speeds up this step significantly. Figure 4depicts the DN
constructed. User is also able to redraw the network according to choice of centrality measures -
, and
. The default measure is
. Once user finalizes the centrality measure and
for the variable selection, Top-
variables are
listed in a dataTable object. nis set by a sliderInput object (see Figure 5).
2Software runs on RStudio.
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Figure 2: Data Import Screen
Figure 3: Data Table
Figure 4: Dependency Network and Setting Centrality Measure
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Figure 5: Finalizing Top-nVariables
Figure 6: Results of mclust Algorithm
After finalizing Top-
variables, various clustering algorithms can be deployed to group data.
mclust Scrucca et al. (2016) and
algorithms are utilized in DiscoVars. Figures 6and 7depict
outputs of mclust and
respectively by using Top-
variables. The default number of cluster
parameter for mclust is 9 and it picks the best grouping with this initial condition. User can pick
number of groups
algorithm (see Figure 7). Elbow plot is also shown in
screen. Clustering results are plotted based on first two principal components in Figures 6and 7.
factoextra package is used for this purpose. Notice that the whole process is interactive. Even if
variable selection method,
, is changed DN will be reconstructed and Top-
variables will be updated
The most critical and time consuming part of DiscoVars is constructing DNs via
regression models.
DiscoVars can easily be used to filter important variables for numerical datasets. In order to report
running times of DiscoVars for DN construction, we chose two different domains: anthropometric
data and crypto currency market data. Anthropometry is the study of forms, measures and functional
capacities of human body. We used two different datasets of anthropometric surveys on US military
. Measuring different parts of human body will cost proportionally to the number of
measurements taken. The obvious question would be what measurements are most critical? The
answer to this question requires expert domain knowledge. Note that this type of data is neither a
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Figure 7: Results of k-means Algorithm
Table 1: DN Construction Times for Various Variable Selection Methods
on Representative Data
Dataset m d Stepwise Forward stepAIC Lasso
(sec.) (sec.) (sec.) (sec.)
Ansur Men11174 131 4881 6039 4751 5
Ansur Women12208 131 5780 7008 7805 6
Ansur2 Men24082 93 2835 3391 3381 5
Ansur2 Women21986 93 1972 2286 1001 5
Coin I 1087 24 16 16 4 4
Coin II 926 18 8 8 3 3
classification nor a regression problem. Similar situation may arise in case of remote sensing data:
which measurements are critical? We think that DiscoVars may yield reasonable results for this kind
of cases without expert domain knowledge.
In the second set of domain, crypto currency market data are collected
. Daily return rate of an
asset, rtcan be calculated as:
where vtand vt1are daily closing values of an asset at dates tand t1 respectively.
We collected data and calculated daily return rates of two different sets of crypto currencies. In the
first set, eight crypto coins’ daily returns, first and second lags of these returns between 10-04-2017
and 09-24-2020 are calculated. In the second set, six crypto coins’ daily returns, first and second lags of
these returns between 08-10-2015 and 02-20-2018 are calculated. DiscoVars can principally discover
major influencers among digital coins. Dimensions of both anthropometric and crypto currency
market data are given in Table 1. These datasets are also provided with the software for reproducibility
Table 1also summarizes running times of variable selection methods on anthropometric and
crypto currency market data. DN construction times are reported in seconds. These experiments were
run on a Windows 10 machine with fourth generation i7 processor, 16GB ram and R version 3.6.1. It is
apparent that Lasso has a far better performance than the remaining methods. For small datasets, all
methods can interactively be used in DiscoVars. For larger datasets, users are advised to utilize Lasso.
Notice that parameters of variables selection methods are not tuned extensively in our implementation.
Even stepwise regression can be sped up by lowering entry and exit
values. Interested users can
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Figure 8: Tool for Unsupervised Feature Selection Methods from Literature
easily modify provided code to reflect tuning variable selection methods.
By utilizing DiscoVars on
Coin I
dataset with
centrality measure, Top-5 variables are
, and
. Note that crypto currency datasets
also include lagged returns.
Implementation of clustvarsel and sparcl on Shiny
In order to have a comparison with our methodology, Shiny implementations of both clustvarsel
Scrucca and Raftery (2018) and
Witten and Tibshirani (2010) are also provided. Some infor-
mation about these methods are already given in Section 3. We use a similar design framework for
the implementations of clustvarsel and
. User needs to import dataset first. Only numerical
variables are included for analysis in both implementations. User is advised to remove univariate and
ID variables again.
clustvarsel is based on mclust Scrucca et al. (2016) clustering method. Forward and backward
directions i.e. variable inclusion and exclusion are available in this unsupervised feature selection
method. The number of models parameter,
can also be specified. The default value is 9. As seen in
Figure 8, user can pick search direction and number of clusters (models) parameters for our Shiny
implementation. Once clustvarsel is run, the output is shown to the user in Figure 9like in mclust
results (see Figure 6). Note that variable selected by clustvarsel are shown in the text box at the top of
Figure 9. Since it is a wrapper method on top of mclust,clustvarsel may take longer times for high
dimensional datasets. Inherently mclust is slower than k-means.
As a second method,
Witten and Tibshirani (2010) is implemented in Shiny via RSKC
Package Kondo et al. (2016) in R.
is available as stand alone R package, but RSKC is more
preferable due to seamless integration with Shiny objects. As seen in Figure 8, user is able to choose
number of clusters,
parameters for RSKC in our implementation interactively. Our initial
experiments have indicated that results are highly sensitive to choice of parameters.
Once RSKC is run, Figure 10 is shown to the user. The selected variables, standard output
of RSKC and clustering plot (first two principal components with cluster labels) are presented.
formulation can be coupled with hierarchical clustering methods too, but
is chosen
as underlying clustering method. As user changes
parameter, different variables may be chosen.
For example, if we reduce
to 1.15 from 1.25, we will get a sparser solution as in Figure 11. If only
are used for clustering BostonHousing dataset, we can get well-partitioned clusters.
A similar result can be achieved with the
centrality measure by DiscoVars. If
is set to 2,
tax and rad will be chosen (see Figure 12).
The methods provided in this section are comparison reason. User can easily compare our
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Figure 9: clustvarsel Results
Figure 10: RSKC Results
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Figure 11: Sparser RSKC Results
Figure 12: Top-2 Variable Results from DiscoVars
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approach, DiscoVars with methods from literature. User can import several datasets to try these
methods interactively.
In this paper, we presented a novel filtering method for learning algorithms. The steps of our general
approach are
i- construction of a graphical model from the data set,
ranking of variables based on a centrality metric calculated by using the constructed graphical
model, and
iii- using the Top-nvariables for the learning algorithm.
In the first step, we employ the well-known graphical model construction methods in the literature.
Although there are studies concentrating on graphical model construction or centrality rankings (i.e.
step (i) or (ii)), to the best of our knowledge, there is no paper combining both steps for the purpose
of filtering for a learning algorithm. In our paper, we applied our new method to two real data sets
(an anthropometric data set and a time series of crypto currency returns) and showed that our new
method successfully returns satisfactory results in a reasonable computing time.
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Ayhan Demiriz
Verikar Software
Pendik, Istanbul, 34912
(ORCiD 0000-0002-5731-3134)
Technical Report, May 2022 Verikar
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Model-based clustering is a popular approach for clustering multivariate data which has seen application in numerous fields. Nowadays, high-dimensional data are more and more common and the model-based clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variable selection techniques has received lot of attention and research effort in recent years. Even for small size problems, variable selection has been advocated to facilitate the interpretation of the clustering results. This review provides a summary of the methods developed for selecting relevant clustering variables in model-based clustering. The methods are illustrated by application to real-world data and existing software to implement the methods are indicated.
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We consider the problem of estimating a network's eigenvector centrality only from data on the nodes, with no information about network topology. Leveraging the versatility of graph filters to model network processes, data supported on the nodes is modeled as a graph signal obtained via the output of a graph filter applied to white noise. We seek to simplify the downstream task of centrality ranking by bypassing network topology inference methods and, instead, inferring the centrality structure of the graph directly from the graph signals. To this end, we propose two simple algorithms for ranking a set of nodes connected by an unobserved set of edges. We derive asymptotic and non-asymptotic guarantees for these algorithms, revealing key features that determine the complexity of the task at hand. Finally, we illustrate the behavior of the proposed algorithms on synthetic and real-world datasets.
CRISP-DM (CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still thede factostandard for developing data mining and knowledge discovery projects. However, undoubtedly the field has moved on considerably in twenty years, with data science now the leading term being favoured over data mining. In this paper we investigate whether, and in what contexts, CRISP-DM is still fit for purpose for data science projects. We argue that if the project is goal-directed and process-driven the process model view still largely holds. On the other hand, when data science projects become more exploratory the paths that the project can take become more varied, and a more flexible model is called for. We suggest what the outlines of such a trajectory-based model might look like and how it can be used to categorise data science projects (goal-directed, exploratory or data management). We examine seven real-life exemplars where exploratory activities play an important role and compare them against 51 use cases extracted from the NIST Big Data Public Working Group. We anticipate this categorisation can help project planning in terms of time and cost characteristics.