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Clustering with finite data from semi-parametric mixture distributions

Yong Wang Ian H. Witten Computer Science Department Computer Science Department
University of Waikato, New Zealand University of Waikato, New Zealand

Email: yongwang@cs.waikato.ac.nz Email: ihw@cs.waikato.ac.nz

Abstract Existing clustering methods for the semi-parametric mixture distribution
perform well as the volume of data increases. However, they all suffer from
a serious drawback in finite-data situations: small outlying groups of data
points can be completely ignored in the clusters that are produced, no matter
how far away they lie from the major clusters. This can result in unbounded
loss if the loss function is sensitive to the distance between clusters.

This paper proposes a new distance-based clustering method that overcomes
the problem by avoiding global constraints. Experimental results illustrate
its superiority to existing methods when small clusters are present in finite
data sets; they also suggest that it is more accurate and stable than other
methods even when there are no small clusters.

1 Introduction A common practical problem is to fit an underlying statistical
distribution to a sample. In some applications, this involves estimating
the parameters of a single distribution function--e.g. the mean and variance
of a normal distribution. In others, an appropriate mixture of elementary
distributions must be found--e.g. a set of normal distributions, each with
its own mean and variance. Among many kinds of mixture distribution, one
in particular is attracting increasing research attention because it has
many practical applications: the semiparametric mixture distribution.

A semi-parametric mixture distribution is one whose cumulative distribution
function (CDF) has the form

FG(x) = Z

\Theta  F (x; `) dG(`); (1)

where ` 2 \Theta , the parameter space, and x 2 X , the sample space. This
gives the CDF of the mixture distribution FG(x) in terms of two more elementary
distributions: the component distribution F (x; `), which is given, and the
mixing distribution G(`), which is unknown. The former has a single unknown
parameter `, while the latter gives a CDF for `. For example, F (x; `) might
be the normal distribution with mean ` and unit variance, where ` is a random
variable distributed according to G(`).

The problem that we will address is the estimation of G(`) from sampled data
that are independent and identically distributed according to the unknown
distribution FG(x). Once G(`) has been obtained, it is a straightforward
matter to obtain the mixture distribution.

The CDF G(`) can be either continuous or discrete. In the latter case, G(`)
is composed of a number of mass points, say, `1; : : : ; `k with masses w1;
: : : ; wk respectively, satisfying Pki=1 wi = 1. Then (1) can be re-written
as

FG(x) =

kX

i=1

wiF (x; `i); (2)

each mass point providing a component, or cluster, in the mixture with the
corresponding weight. If the number of components k is finite and known a
priori, the mixture distribution is called finite; otherwise it is treated
as countably infinite. The qualifier "countably" is necessary to distinguish
this case from the situation with continuous G(`), which is also infinite.

We will focus on the estimation of arbitrary mixing distributions, i.e.,
G(`) is any general probability distribution--finite, countably infinite
or continuous. A few methods for tackling this problem can be found in the
literature. However, as we shall see, they all suffer from a serious drawback
in finite-data situations: small outlying groups of data points can be completely
ignored in the clusters that are produced.

This phenomenon seems to have been overlooked, presumably for three reasons:
small amounts of data may be assumed to represent a small loss; a few data
points

1

can easily be dismissed as outliers; and in the limit the problem evaporates
because most estimators possess the property of strong consistency--which
means that, almost surely, they converge weakly to any given G(`) as the
sample size approaches infinity. However, often these reasons are inappropriate:
the loss function may be sensitive to the distance between clusters; the
small number of outlying data points may actually represent small clusters;
and any practical clustering situation will necessarily involve finite data.

This paper proposes a new method, based on the idea of local fitting, that
successfully solves the problem. The experimental results presented below
illustrate its superiority to existing methods when small clusters are present
in finite data sets. Moreover, they also suggest that it is more accurate
and stable than other methods even when there are no small clusters. Existing
clustering methods for semi-parametric mixture distributions are briefly
reviewed in the next section. Section 3 identifies a common problem from
which these current methods suffer. Then we present the new solution, and
in Section 5 we describe experiments that illustrate the problem that has
been identified and show how the new method overcomes it.

2 Clustering methods The general problem of inferring mixture models is treated
extensively and in considerable depth in books by Titterington et al. (1985),
McLachlan and Basford (1988) and Lindsay (1995). For semi-parametric mixture
distributions there are three basic approaches: minimum distance, maximum
likelihood, and Bayesian. We briefly introduce the first approach, which
is the one adopted in the paper, review the other two to show why they are
not suitable for arbitrary mixtures, and then return to the chosen approach
and review the minimum distance estimators for arbitrary semi-parametric
mixture distributions that have been described in the literature.

The idea of the minimum distance method is to define some measure of the
goodness of the clustering and optimize this by suitable choice of a mixing
distribution Gn(`) for a sample of size n. We generally want the estimator
to be strongly consistent as n ! 1, in the sense defined above, for arbitrary
mixing distributions. We also generally want to take advantage of the special
structure of semi-parametric mixtures to come up with an efficient algorithmic
solution.

The maximum likelihood approach maximizes the likelihood (or equivalently
the log-likelihood) of the data by suitable choice of Gn(`). It can in fact
be viewed as

a minimum distance method that uses the Kullback- Leibler distance (Titterington
et al., 1985). This approach has been widely used for estimating finite mixtures,
particularly when the number of clusters is fairly small, and it is generally
accepted that it is more accurate than other methods. However, it has not
been used to estimate arbitrary semi-parametric mixtures, presumably because
of its high computational cost. Its speed drops dramatically as the number
of parameters that must be determined increases, which makes it computationally
infeasible for arbitrary mixtures, since each data point might represent
a component of the final distribution with its own parameters.

Bayesian methods assume prior knowledge, often given by some kind of heuristic,
to determine a suitable a priori probability density function. They are often
used to determine the number of components in the final distribution--particularly
when outliers are present. Like the maximum likelihood approach they are
computationally expensive, for they use the same computational techniques.

We now review existing minimum distance estimators for arbitrary semi-parametric
mixture distributions. We begin with some notation. Let x1; : : : ; xn be
a sample chosen according to the mixture distribution, and suppose (without
loss of generality) that the sequence is ordered so that x1 ^ x2 ^ : : :
^ xn. Let Gn(`) be a discrete estimator of the underlying mixing distribution
with a set of support points at f`nj; j = 1; : : :; kng. Each `nj provides
a component of the final clustering with

weight wnj * 0, where Pk

n

j=1 wnj = 1. Given the sup-port points, obtaining G

n(`) is equivalent to computing the weight vector wn = (wn1; wn2; : : :;
wnk

n)0. Denoteby F

Gn(x) the estimated mixture CDF with respect to Gn(`).

Two minimum distance estimators were proposed in the late 1960s. Choi and
Bulgren (1968) used

1 n

nX

i=1

[FG

n(xi) \Gamma  i=n]

2 (3)

as the distance measure. Minimizing this quantity with respect to Gn yields
a strongly consistent estimator. A slight improvement is obtained by using
the Cram'er-von Mises statistic

1 n

nX

i=1

[FG

n(xi) \Gamma  (i \Gamma  1=2)=n]

2 + 1=(12n2); (4)

which essentially replaces i=n in (3) with (i \Gamma  12 )=n without affecting
the asymptotic result. As might be expected, this reduces the bias for small-sample
cases, as

was demonstrated empirically by Macdonald (1971) in a note on Choi and Bulgren's
paper.

At about the same time, Deely and Kruse (1968) used the sup-norm associated
with the Kolmogorov-Smirnov test. The minimization is over

sup 1^i^nfjF

Gn(xi) \Gamma  (i \Gamma  1)=nj; jFGn(xi) \Gamma  i=njg; (5)

and this leads to a linear programming problem. Deely and Kruse also established
the strong consistency of their estimator Gn. Ten years later, this approach
was extended by Blum and Susarla (1977) by using any sequence ffng of functions
which satisfies sup jfn\Gamma fGj ! 0 a.s. as n ! 1. Each fn can, for example,
be obtained by a kernel-based density estimator. Blum and Susarla approximated
the function fn by the overall mixture pdf fG

n , and established the strong consistency of the esti-mator G

n under weak conditions.

For reason of simplicity and generality, we will denote the approximation
between two mathematical entities of the same type by ,=, which implies the
minimization with respect to an estimator of a distance measure between the
entities on either side. The types of entity involved in this paper include
vector, function and measure, and we use the same symbol ,= for each.

In the work reviewed above, two kinds of estimator are used: CDF-based (Choi
and Bulgren, Macdonald, and Deely and Kruse) and pdf-based (Blum and Susarla).
CDF-based estimators involve approximating an empirical distribution with
an estimated one FG

n. We writethis as

FG

n ,= Fn; (6)

where Fn is the Kolmogorov empirical CDF--or indeed any empirical CDF that
converges to it. Pdf-based estimators involve the approximation between probability
density functions:

fG

n ,= fn; (7)

where fG

n is the estimated mixture pdf and fn is theempirical pdf described above.

The entities involved in (6) and (7) are functions. When the approximation
is computed, however, it is computed between vectors that represent the functions.
These vectors contain the function values at a particular set of points,
which we call "fitting points." In the work reviewed above, the fitting points
are chosen to be the data points themselves.

3 The problem of minority clusters

Although they perform well asymptotically, all the minimum distance methods
described above suffer from the finite-sample problem discussed earlier:
they can neglect small groups of outlying data points no matter how far they
lie from the dominant data points. The underlying reason is that the objective
function to be minimized is defined globally rather than locally. A global
approach means that the value of the estimated probability density function
at a particular place will be influenced by all data points, no matter how
far away they are. This can cause small groups of data points to be ignored
even if they are a long way from the dominant part of the data sample. From
a probabilistic point of view, however, there is no reason to subsume distant
groups within the major clusters just because they are relatively small.

The ultimate effect of suppressing distant minority clusters depends on how
the clustering is applied. If the application's loss function depends on
the distance between clusters, the result may prove disastrous because there
is no limit to how far away these outlying groups may be. One might argue
that small groups of points can easily be explained away as outliers, because
the effect will become less important as the number of data points increases--and
it will disappear in the limit of infinite data. However, in a finite-data
situation--and all practical applications necessarily involve finite data--the
"outliers" may equally well represent small minority clusters. Furthermore,
outlying data points are not really treated as outliers by these methods--whether
or not they are discarded is merely an artifact of the global fitting calculation.
When clustering, the final mixture distribution should take all data points
into account--including outlying clusters if any exist. If practical applications
demand that small outlying clusters are suppressed, this should be done in
a separate stage.

In distance-based clustering, each data point has a farreaching effect because
of two global constraints. One is the use of the cumulative distribution
function; the other is the normalization constraint Pk

n

j=1 wnj = 1. Theseconstraints may sacrifice a small number of data points--

at any distance--for a better overall fit to the data as a whole. Choi and
Bulgren (1968), the Cramer-von Mises statistic (Macdonald, 1971), and Deely
and Kruse (1968) all enforce both the CDF and the normalization constraints.
Blum and Susarla (1977) drop the CDF, but still enforce the normalization
constraint. The result is that these clustering methods are only appropriate
for finite mixtures without small clusters, where the risk of suppressing
clusters is low.

This paper addresses the general problem of arbitrary mixtures. Of course,
the minority cluster problem exists for all types of mixture--including finite
mixtures. Even here, the maximum likelihood and Bayesian approaches do not
solve the problem, because they both introduce a global normalization constraint.

4 Solving the minority cluster

problem

Now that the source of the problem has been identified, the solution is clear,
at least in principle: drop both the approximation of CDFs, as Blum and Susarla
(1977) do, and the normalization constraint--no matter how seductive it may
seem.

Let G0n be a discrete function with masses fwnjg at f`njg; note that we do
not require the wnj to sum to one. Since the new method operates in terms
of measures rather than distribution functions, the notion of approximation
is altered to use intervals rather than points. Using the formulation described
in Section 2, we have

PG0

n ,= Pn; (8)

where PG0

n is the estimated measure and Pn is the em-pirical measure. The intervals
over which the approximation takes place are called "fitting intervals."
Since (8) is not subject to the normalization constraint, G0n is not a CDF
and PG0

n is not a probability measure. How-ever, G0

n can be easily converted into a CDF estimatorby normalizing it after equation
(8) has been solved.

To define the estimation procedure fully, we need to determine (a) the set
of support points, (b) the set of fitting intervals, (c) the empirical measure,
and (d) the distance measure. Here we discuss these in an intuitive manner;
Wang and Witten (1999) show how to determine them in a way that guarantees
a strongly consistent estimator.

Support points. The support points are usually suggested by the data points
in the sample. For example, if the component distribution F (x; `) is the
normal distribution with mean ` and unit variance, each data point can be
taken as a support point. In fact, the support points are more accurately
described as potential support points, because their associated weights may
become zero after solving (8)--and, in practice, many often do.

Fitting intervals. The fitting intervals are also suggested by the data points.
In the normal distribution example, each data point xi can provide one interval,
such as [xi \Gamma  3oe; xi], or two, such as [xi \Gamma  3oe; xi] and [xi;
xi + 3oe], or more. There is no problem if the fitting

intervals overlap. Their length should not be so large that points can exert
an influence on the clustering at an unduly remote place, nor so small that
the empirical measure is inaccurate. The experiments reported below use intervals
of a few standard deviations around each data point, and, as we will see,
this works well.

Empirical measure. The empirical measure can be the probability measure determined
by the Kolmogorov empirical CDF, or any measure that converges to it. The
fitting intervals discussed above can be open, closed, or semi-open. This
will affect the empirical measure if data points are used as interval boundaries,
although it does not change the values of the estimated measure because the
corresponding distribution is continuous. In smallsample situations, bias
can be reduced by careful attention to this detail--as Macdonald (1971) discusses
with respect to Choi and Bulgren's (1968) method.

Distance measure. The choice of distance measure determines what kind of
mathematical programming problem must be solved. For example, a quadratic
distance will give rise to a least squares problem under linear constraints,
whereas the sup-norm gives rise to a linear programming problem that can
be solved using the simplex method. These two measures have efficient solutions
that are globally optimal.

It is worth pointing out that abandoning the global constraints associated
with both CDFs and normalization can brings with it a computational advantage.
In vector form, we write PG0

n = AG

0 nwn, where wn is the(unnormalized) weight vector and each element of the

matrix AG0

n is the probability value of a component dis-tribution over an fitting interval.
Then, provided the

support points corresponding to w0n and w00n lie outside each others' sphere
of influence as determined by the component distributions F (x; `), the estimation
procedure becomes`

A0G0

n 00 A00

G0n ' `

w0n w00n ' ,= `

P 0n P 00n ' ; (9)

subject to w0n * 0 and w00n * 0. This is the same as combining the solutions
of two sub-equations, A0nw0n ,= P 0n subject to w0n * 0, and A00nw00n ,=
P 00n subject to w00n * 0. If the relevant support points continue to lie
outside each others' sphere of influence, the sub-equations can be further
partitioned. This implies that when data points are sufficiently far apart,
the mixing distribution G can be estimated by grouping data points in different
regions. Moreover, the solution in each region can be normalized separately
before they are combined, which yields a better estimation of the mixing
distribution.

If the normalization constraint Pk

n

j=1 wnj = 1 is re-tained when estimating the mixing distribution, the es
timation procedure becomes

PG

n ,= Pn: (10)

where the estimator Gn is a discrete CDF on \Theta . This constraint is necessary
for the left-hand side of (10) to be a probability measure. Although he did
not develop an operational estimation scheme, Barbe (1998) suggested exploiting
the fact that the empirical probability measure is approximated by the estimated
probability measure--but he retained the normalization constraint. As noted
above, relaxing the constraint has the effect of loosening the throttling
effect of large clusters on small groups of outliers, and our experimental
results show that the resulting estimator suffers from the drawback noted
earlier.

Both estimators, Gn obtained from (10) and G0n from (8), have been shown
to be strongly consistent under weak conditions similar to those used by
others (Wang & Witten, 1999). Of course, the weak convergence of G0n is in
the sense of general functions, not CDFs. The strong consistency of G0n immediately
implies the strong consistency of the CDF estimator obtained by normalizing
G0n.

5 Experimental validation We have conducted experiments to illustrate the
failure of existing methods to detect small outlying clusters, and the improvement
achieved by the new scheme. The results also suggest that the new method
is more accurate and stable than the others.

When comparing clustering methods, it is not always easy to evaluate the
clusters obtained. To finesse this problem we consider simple artificial
situations in which the proper outcome is clear. Some practical applications
of clusters do provide objective evaluation functions; however, these are
beyond the scope of this paper.

The methods used are Choi and Bulgren (1968) (denoted choi), Macdonald's
application of the Cram'er-von Mises statistic (cram'er), the new method
with the normalization constraint (test), and the new method without that
constraint (new). In each case, equations involving non-negativity and/or
linear equality constraints are solved as quadratic programming problems
using the elegant and efficient procedures nnls and lsei provided by Lawson
and Hanson (1974). All four methods have the same computational time complexity.

We set the sample size n to 100 throughout the experiments. The data points
are artificially generated from a mixture of two clusters: n1 points from
N (0; 1) and n2 points from N (100; 1). The values of n1 and n2 are in the
ratios 99 : 1, 97 : 3, 93 : 7, 80 : 20 and 50 : 50.

Every data point is taken as a potential support point in all four methods:
thus the number of potential components in the clustering is 100. For test
and new, fitting intervals need to be determined. In the experiments, each
data point xi provides the two fitting intervals [xi \Gamma  3; xi] and [xi;
xi + 3]. Any data point located on the boundary of an interval is counted
as half a point when determining the empirical measure over that interval.

These choices are admittedly crude, and further improvements in the accuracy
and speed of test and new are possible that take advantage of the flexibility
provided by (10) and (8). For example, accuracy will likely increase with
more--and more carefully chosen-- support points and fitting intervals. The
fact that it performs well even with crudely chosen support points and fitting
intervals testifies to the robustness of the method.

Our primary interest in this experiment is the weights of the clusters that
are found. To cast the results in terms of the underlying models, we use
the cluster weights to estimate values for n1 and n2. Of course, the results
often do not contain exactly two clusters--but because the underlying cluster
centres, 0 and 100, are well separated compared to their standard deviation
of 1, it is highly unlikely that any data points from one cluster will fall
anywhere near the other. Thus we use a threshold of 50 to divide the clusters
into two groups: those near 0 and those near 100. The final cluster weights
are normalized, and the weights for the first group are summed to obtain
an estimate ^n1 of n1, while those for the second group are summed to give
an estimate ^n2 of n2.

Table 1 shows results for each of the four methods. Each cell represents
one hundred separate experimental runs. Three figures are recorded. At the
top is the number of times the method failed to detect the smaller cluster,
that is, the number of times ^n2 = 0. In the middle are the average values
for ^n1 and ^n2. At the bottom is the standard deviation of ^n1 and ^n2 (which
are equal). These three figures can be thought of as measures of reliability,
accuracy and stability respectively.

The top figures in Table 1 show clearly that only new is always reliable
in the sense that it never fails to detect the smaller cluster. The other
methods fail mostly when n2 = 1; their failure rate gradually decreases as
n2 grows. The center figures show that, under all conditions, new gives a
more accurate estimate of the correct values of n1 and n2 than the other
methods. As expected, cram'er shows a noticeable improvement over choi, but
it is very minor. The test method has lower failure rates and produces estimates
that are more accurate and far more stable (indicated by the bottom fign1
= 99 n1 = 97 n1 = 93 n1 = 80 n1 = 50

n2 = 1 n2 = 3 n2 = 7 n2 = 20 n2 = 50 choi Failures 86 42 4 0 0

^n1=^n2 99.9/0.1 99.2/0.8 95.8/4.2 82.0/18.0 50.6/49.4 SD(^n1) 0.36 0.98
1.71 1.77 1.30 cram'er Failures 80 31 1 0 0

^n1=^n2 99.8/0.2 98.6/1.4 95.1/4.9 81.6/18.4 49.7/50.3 SD(^n1) 0.50 1.13
1.89 1.80 1.31 test Failures 52 5 0 0 0

^n1=^n2 99.8/0.2 98.2/1.8 94.1/5.9 80.8/19.2 50.1/49.9 SD(^n1) 0.32 0.83
0.87 0.78 0.55 new Failures 0 0 0 0 0

^n1=^n2 99.0/1.0 96.9/3.1 92.8/7.2 79.9/20.1 50.1/49.9 SD(^n1) 0.01 0.16
0.19 0.34 0.41

Table 1: Experimental results for detecting small clusters ures) than those
for choi and cram'er--presumably because it is less constrained. Of the four
methods, new is clearly and consistently the winner in terms of all three
measures: reliability, accuracy and stability.

The results of the new method can be further improved. If the decomposed
form (9) is used instead of (8), and the solutions of the sub-equations are
normalized before combining them--which is feasible because the two underlying
clusters are so distant from each other--the correct values are obtained
for ^n1 and ^n2 in virtually every trial.

6 Conclusions We have identified a shortcoming of existing clustering methods
for arbitrary semi-parametric mixture distributions: they fail to detect
very small clusters reliably. This is a significant weakness when the minority
clusters are far from the dominant ones and the loss function takes account
of the distance of misclustered points.

We have described a new clustering method for arbitrary semi-parametric mixture
distributions, and shown experimentally that it overcomes the problem. Furthermore,
the experiments suggest that the new estimator is more accurate and more
stable than existing ones.

References Barbe, P. (1998). Statistical analysis of mixtures and

the empirical probability measure. Acta Applicandae Mathematicae, 50(3),
253-340.

Blum, J. R. & Susarla, V. (1977). Estimation of a mixing

distribution function. Ann. Probab, 5, 200-209.

Choi, K. & Bulgren, W. B. (1968). An estimation procedure for mixtures of
distributions. J. R. Statist. Soc. B, 30, 444-460.

Deely, J. J. & Kruse, R. L. (1968). Construction of sequences estimating
the mixing distribution. Ann. Math. Statist., 39, 286-288.

Lawson, C. L. & Hanson, R. J. (1974). Solving Least

Squares Problems. Prentice-Hall, Inc.

Lindsay, B. G. (1995). Mixture models: theory, geometry,

and applications, Volume 5 of NSF-CBMS Regional Conference Series in Probability
and Statistics. Institute for Mathematical Statistics: Hayward, CA.

Macdonald, P. D. M. (1971). Comment on a paper by

Choi and Bulgren. J. R. Statist. Soc. B, 33, 326- 329.

McLachlan, G. & Basford, K. (1988). Mixture Models:

Inference and Applications to Clustering. Marcel Dekker, New York.

Titterington, D. M., Smith, A. F. M. & Makov, U. E.

(1985). Statistical Analysis of Finite Mixture Distributions. John Wiley
& Sons.

Wang, Y. & Witten, I. H. (1999). The estimation of mixing distributions by
approximating empirical measures. Technical Report (in preparation), Dept.
of Computer Science, University of Waikato, New Zealand.
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