A PROBABILISTIC ℓ1 METHOD FOR CLUSTERING HIGH DIMENSIONAL DATA

arXiv:1504.01294v2 [] 22 Apr 2016

TSVETAN ASAMOV AND ADI BEN–ISRAEL
Abstract. In general, the clustering problem is NP–hard, and global optimality cannot be established for
non–trivial instances. For high–dimensional data, distance–based methods for clustering or classification
face an additional difficulty, the unreliability of distances in very high–dimensional spaces. We propose a
probabilistic, distance–based, iterative method for clustering data in very high–dimensional space, using the
ℓ1 –metric that is less sensitive to high dimensionality than the Euclidean distance. For K clusters in Rn ,
the problem decomposes to K problems coupled by probabilities, and an iteration reduces to finding Kn
weighted medians of points on a line. The complexity of the algorithm is linear in the dimension of the data
space, and its performance was observed to improve significantly as the dimension increases.

1. Introduction
The emergence and growing applications of big data have underscored the need for efficient algorithms
based on optimality principles, and scalable methods that can provide valuable insights at a reasonable
computational cost.
In particular, problems with high–dimensional data have arisen in several scientific and technical areas
(such as genetics [19], medical imaging [29] and spatial databases [21], etc.) These problems pose a special
challenge because of the unreliability of distances in very high dimensions. In such problems it is often
advantageous to use the ℓ1 –metric which is less sensitive to the “curse of dimensionality” than the Euclidean
distance.
We propose a new probabilistic distance–based method for clustering data in very high–dimensional
spaces. The method uses the ℓ1 –distance, and computes the cluster centers using weighted medians of
the given data points. Our algorithm resembles well–known techniques such as fuzzy clustering [9] and
K–means, and inverse distance interpolation [26].
The cluster membership probabilities are derived from necessary optimality conditions for an approximate problem, and decompose a clustering problem with K clusters in Rn into Kn one–dimensional
problems, which can be solved separately. The algorithm features a straightforward implementation and
a polynomial running time, in particular, its complexity is linear in the dimension n. In numerical experiments it outperformed several commonly used methods, with better results for higher dimensions.
While the cluster membership probabilities simplify our notation, and link our results to the theory of
subjective probability, these probabilities are not needed by themselves, since they are given in terms of
distances, that have to be computed at each iteration.
1.1. Notation. We use the abbreviation 1,K := {1, 2, . . . , K} for the indicated index set. The j th component of a vector x i ∈ Rn is denoted x i [j]. The ℓp –norm of a vector x = (x[j]) ∈ Rn is
n
X
p
kxk p := (
x[j] )1/p
j=1

Date: April 7, 2016.
2010 Mathematics Subject Classification. Primary 62H30, 90B85; Secondary 90C59.
Key words and phrases. Clustering, ℓ1 –norm, high–dimensional data, continuous location.
1

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TSVETAN ASAMOV AND ADI BEN–ISRAEL

and the associated ℓp –distance between two vectors x and y is dp (x, y) := kx − ykp , in particular, the
Euclidean distance with p = 2, and the ℓ1 –distance,
n
X
d1 (x, y) = kx − yk1 =
x[j] − y[j] .
(1)
j=1

1.2. The clustering problem. Given
• a set X = {x i : i ∈ 1,N } ⊂ Rn of N points x i in Rn ,
• their weights W = {w i > 0 : i ∈ 1,N }, and
• an integer 1 ≤ K ≤ N ,
partition X into K clusters {X k : k ∈ 1,K}, defined as disjoint sets where the points in each cluster are
similar (in some sense), and points in different clusters are dissimilar. If by similar is meant close in some
metric d(x, y), we have a metric (or distance based) clustering problem, in particular ℓ1 –clustering
if the ℓ1 –distance is used, Euclidean clustering for the ℓ2 –distance, etc.
1.3. Centers. In metric clustering each cluster has a representative point, or center, and distances to
clusters are defined as the distances to their centers. The center c k of cluster X k is a point c that minimizes
the sum of weighted distances to all points of the cluster,
 X
w i d(x i , c) .
(2)
c k := arg min
x i ∈Xk

Thus, the metric clustering problem can be formulated as follows: Given X, W and K as above, find centers
{c k : k ∈ 1,K} ⊂ Rn so as to minimize
min

c1 ,··· ,cK

K
X
X

w i d(x i , c k ),

(L.K)

k=1 x i ∈Xk

where X k is the cluster of points in X assigned to the center c k .
1.4. Location problems. Metric clustering problems often arise in location analysis, where X is the set
of the locations of customers, W is the set of their weights (or demands), and it is required to locate
K facilities {c k } to serve the customers optimally in the sense of total weighted-distances traveled. The
problem (L.K) is then called a multi–facility location problem, or a location–allocation problem
because it is required to locate the centers, and to assign or allocate the points to them.
Problem (L.K) is trivial for K = N (every point is its own center) and reduces for K = 1 to the single
facility location problem: find the location of a center c ∈ Rn so as to minimize the sum of weighted
distances,
N
X
w i d(x i , c).
(L.1)
min n
c ∈ R i=1
For 1 < K < N , the problem (L.K) is NP-hard in general [24], while the planar case can be solved
polynomially in N , [13].
1.5. Probabilistic approximation. (L.K) can be approximated by a continuous problem
min

c1 ,··· ,cK

K
X
X

w i pk (x i )d(x i , c k ),

(P.K)

k=1 x i ∈X

where rigid assignments x i ∈ X k are replaced by probabilistic (soft) assignments, expressed by probabilities
pk (x i ) that a point x i belongs to the cluster X k .

HIGH DIMENSIONAL CLUSTERING

3

For each point x i the cluster membership probabilities pk (x i ) sum to 1, and are assumed to depend
on the distances d(x i , c k ) as follows
membership in a cluster is more likely the closer is its center

(A)

Given these probabilities, the problem (P.K) can be decomposed into K single facility location problems,
X
min
pk (x i )w i d(x i , c k ),
(P.k)
ck

x i ∈X

for k ∈ 1,K. The solutions c k of the K problems (P.k), are then used to calculate the new distances
d(x i , c k ) for all i ∈ 1,N , k ∈ 1,K, and from them, new probabilities {pk (x i )}, etc.
1.6. The case for the ℓ1 norm. In high dimensions, distances between points become unreliable [7], and
this in particular “makes a proximity query meaningless and unstable because there is poor discrimination
between the nearest and furthest neighbor” [1]. For the Euclidean distance
n
n
X
X
2 1/2
2
x[j] y[j] + kyk22 )1/2
(3)
x
[j]
−
y
[j]
)
=
(kxk
−
2
d2 (x, y) = (
2
j=1

j=1

between random points x, y ∈
consequently,

Rn ,

the cross products x[j] y[j] in (3) tend to cancel for very large n, and

d2 (x, y) ≈ (kxk22 + kyk22 )1/2 .
√
In particular, if x, y are random points on the unit sphere in Rn then d2 (x, y) ≈ 2 for very large n. This
“curse of high dimensionality” limits the applicability of distance based methods in high dimension.
The ℓ1 –distance is less sensitive to high dimensionality, and has been shown to “provide the best discrimination in high–dimensional data spaces”, [1]. We use it throughout this paper.
The plan of the paper. The ℓ1 –metric clustering problem is solved in § 2 for one center. A probabilistic approximation of (L.K) is discussed in § 3, the probabilities studied in §§ 4–5. The centers of the
approximate problem are computed in § 6. Our main result, Algorithm PCM(ℓ1 ) of § 8, uses the power
probabilities of § 7, and has running time that is linear in the dimension of the space, see Corollary 1.
Theorem 1, a monotonicity property of Algorithm PCM(ℓ1 ), is proved in § 9. Section 10 lists conclusions.
Appendix A shows relations to previous work, and Appendix B reports some numerical results.
2. The single facility location problem with the ℓ1 –norm
For the ℓ1 –distance (1) the problem (L.1) becomes
min

c∈Rn

N
X

w i d1 (xi , c),

i=1

or

min

c∈Rn

N
X

wi

i=1

n
X
j=1

x i [j] − c[j] ,

(4)

in the variable c ∈ Rn , which can be solved separately for each component c[j], giving the n problems
min

c[j]∈R

N
X
i=1

w i x i [j] − c[j] , j ∈ 1,n.

(5)

Definition 1. Let X = {x 1 , · · · , x N } ∈ R be an ordered set of points
x1 ≤ x2 ≤ · · · ≤ xN

and let W = {w 1 , · · · , w N } be a corresponding set of positive weights. A point x is a weighted median
(or W–median) of X if there exist α, β ≥ 0 such that
X
X
{w i : x i < x} + α =
{w i : x i > x} + β
(6)

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TSVETAN ASAMOV AND ADI BEN–ISRAEL

where α + β is the weight of x if x ∈ X, and α = β = 0 if x 6∈ X.
The weighted median always exists, but is not necessarily unique.
Lemma 1. For X, W as above, define

θ k :=

k
P

i=1
N
P

wi
k ∈ 1,N ,

,
wi

(7)

i=1

and let k∗ be the smallest k with θ k ≥ 12 . If

θ k∗ >

1
2

(8)

then x k∗ is the unique weighted median, with
!
X
X
1
α = 2 w k∗ +
wk −
w k , β = w k∗ − α.
k>k ∗

(9)

k<k ∗

Otherwise, if

θ k∗ = 21 ,
then any point in the open interval (x k∗ , x k∗+1 ) is a weighted median with α = β = 0.
Proof. The statement holds since the sequence (7) is increasing from θ1 = (w1 /
Note: In case (10),
X

{w k : x k ≤ x k∗ } =

X

PN

k=1

(10)

wk ) to θN = 1.



{w k : x k ≥ x k∗+1 },

we can take the median as the midpoint of x k∗ and x k∗+1 , in order to conform with the classical definition
of the median (for an even number of points of equal weight) .
Lemma 2. Given X and W as in Definition 1, the set of minimizers c of
N
X
i=1

is the set of W–medians of X.

wi xi − c

Proof. The result is well known if all weights are 1. If the weights are integers, consider a point x i with
weight w i as w i coinciding points of weight 1 and the result follows. Same if the weights are rational.
Finally, if the weights are real, consider their rational approximations.

3. Probabilistic approximation of (L.K)
We relax the assignment problem in (L.K) of § 1.2 by using a continuous approximation as follows,
min

N
K X
X

w i p k (x i ) d(x i , c k )

(P.K)

k=1 i=1

with two sets of variables,
the centers {c k }, and
the cluster membership probabilities {p k (x i )},

p k (x i ) := Prob {x i ∈ X k }, i ∈ 1,N , k ∈ 1,K,

(11)

HIGH DIMENSIONAL CLUSTERING

5

Because the probabilities {p k (x i )} add to 1 for each i ∈ 1,N , the objective function of (P.K) is an upper
bound on the optimal value of (L.K),
K X
N
X
k=1 i=1

w i p k (x i ) d(x i , c k ) ≥ min (L.K),

(12)

and therefore so is the optimal value of (P.K),
min (P.K) ≥ min (L.K).

(13)

4. Axioms for probabilistic distance clustering
In this section, dk (x) stands for dk (x, ck ), the distance of x to the center ck of the k th –cluster, k ∈ 1,K.
To simplify notation, the point x is assumed to have weight w = 1.
The cluster membership probabilities {pk (x) : k ∈ 1,K} of a point x depend only on the distances
{dk (x) : k ∈ 1,K},
p(x) = f (d(x))
(14)
where p(x) ∈ RK is the vector of probabilities (pk (x)), and d(x) is the vector of distances (dk (x)). Natural
assumptions for the relation (14) include
di (x) < dj (x) =⇒ pi (x) > pj (x), for all i, j ∈ 1,K
f (λ d(x)) = f (d(x)), for any λ > 0

Q p(x) = f (Q d(x)), for any permutation matrices Q

(15a)
(15b)
(15c)

Condition (15a) states that membership in a cluster is more probable the closer it is, which is Assumption
(A) of § 1.5. The meaning of (15b) is that the probabilities pk (x) do not depend on the scale of measurement,
i.e., f is homogeneous of degree 0. It follows that the probabilities pk (x) depend only on the ratios of the
distances {dk (x) : k ∈ 1,K}.
The symmetry of f , expressed by (15c), guarantees for each k ∈ 1, K, that the probability pk (x) does
not depend on the numbering of the other clusters.
Assuming continuity of f it follows from (15a) that
di (x) = dj (x) =⇒ pi (x) = pj (x),
for any i, j ∈ 1, K.
For any nonempty subset S ⊂ 1, K, let
pS (x) =

X

ps (x),

s∈S

the probability that x belongs to one of the clusters {Cs : s ∈ S}, and let pk (x|S) denote the conditional
probability that x belongs to the cluster Ck , given that it belongs to one of the clusters {Cs : s ∈ S}.
Since the probabilities pk (x) depend only on the ratios of the distances {dk (x) : k ∈ 1,K}, and these
ratios are unchanged in subsets S of the index set 1,K, it follows that for all k ∈ 1, K, ∅ =
6 S ⊂ 1,K,
pk (x) = pk (x|S) pS (x)

(16)

which is the choice axiom of Luce, [22, Axiom 1], and therefore, [30],
vk (x)
pk (x|S) = P
vs (x)
s∈S

(17)

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TSVETAN ASAMOV AND ADI BEN–ISRAEL

where vk (x) is a scale function, in particular,
vk (x)
.
pk (x) = P
vs (x)

(18)

s∈1,K

Assuming vk (x) 6= 0 for all k, it follows that

pk (x)vk (x)−1 = P

1
,
vs (x)

(19)

s∈1,K

where the right hand side is a function of x, and does not depend on k.
Property (15a) implies that the function vk (·) is a monotone decreasing function of dk (x).
5. Cluster membership probabilities as functions of distance
Given K centers {c k }, and a point x with weight w and distances {d(x, c k ) : k ∈ 1,K} from these
centers, a simple choice for the function vk (x) in (17) is
1
,
(20)
vk (x) =
w dk (x)
for which (19) gives1,
w p k (x) d(x, c k ) = D(x), k ∈ 1,K,
(21)
where the function D(x), called the joint distance function (JDF) at x, does not depend on k.
For a given point x and given centers {ck }, equations (21) are optimality conditions for the extremum
problem
)
( K
K
X
X
(22)
min w
p2k d(x, ck ) :
pk = 1, pk ≥ 0, k ∈ 1,K
k=1

k=1

in the probabilities {p k := p k (x)}. The squares of probabilities in the objective of (22) serve to smooth
the underlying non–smooth problem, see the seminal paper by Teboulle [27]. Indeed, (21) follows by
differentiating the Lagrangian
!
K
K
X
X
L(p, λ) = w
p2k d(x, c k ) + λ
pk − 1 ,
(23)
k=1

k=1

with respect to p k and equating the derivative to zero.
Since probabilities add to one we get from (21),
Q
d(x, c j )
pk (x) =

j6=k

K
P

Q

d(x, c m )

, k ∈ 1,K,

(24)

ℓ=1 m6=ℓ

and the JDF at x,

D(x) = w

K
Q

d(x, c j )

j=1
K Q
P

,

(25)

d(x, c m )

ℓ=1 m6=ℓ

which is (up to a constant) the harmonic mean of the distances {d(x, c k ) : k ∈ 1,K}, see also (A-4)
below.
1There are other ways to model Assumption (A), e.g. [5], but the simple model (21) works well enough for our purposes.

HIGH DIMENSIONAL CLUSTERING

7

Note that the probabilities {p k (x) : k ∈ 1,K} are determined by the centers {c k : k ∈ 1,K} alone, while
the function D(x) depends also on the weight w. For example, in case K = 2,
p 1 (x) =

d(x, c 1 )
d(x, c 2 )
, p 2 (x) =
,
d(x, c 1 ) + d(x, c 2 )
d(x, c 1 ) + d(x, c 2 )
d(x, c 1 ) d(x, c 2 )
D(x) = w
.
d(x, c 1 ) + d(x, c 2 )

(26a)
(26b)

6. Computation of centers
We use the ℓ1 –distance (1) throughout. The objective function of (P.K) is a separable function of the
cluster centers,
f (c 1 , . . . , c K ) :=
where f k (c) :=

K
X

k=1
N
X

f k (c k ),

(27a)

k ∈ 1,K.

(27b)

x i [j] − c k [j] , k ∈ 1,K,

(28)

w i p k (x i ) d 1 (x i , c),

i=1

The centers problem thus separates into K problems of type (4),
min
ck ∈ Rn

N
X

w i p k (x i )

n
X
j=1

i=1

coupled by the probabilities {p k (x i )}. Each of these problems separates into n problems of type (5) for
the components c k [j],
min
ck [j] ∈ R

N
X
i=1

w i p k (x i ) x i [j] − c k [j] , k ∈ 1,K, j ∈ 1,n,

(29)

whose solution, by Lemma 2, is a weighted median of the points {x i [j]} with weights {w i p k (x i )}.
7. Power probabilities
The cluster membership probabilities {pk (x) : k ∈ 1,K} of a point x serve to relax the rigid assignment
of x to any of the clusters, but eventually it may be necessary to produce such an assignment. One way
to achieve this is to raise the membership probabilities pk (x) of (24) to a power ν ≥ 1, and normalize,
obtaining the power probabilities
pν (x)
(ν)
p k (x) := K k
,
(30)
P ν
p j (x)
j=1

which, by (24), can also be expressed in terms of the distances d(x, c k ),
Q
d(x, c j )ν
(ν)

p k (x) :=

j6=k

K
P

Q

d(x, c m

)ν

, k ∈ 1,K.

(31)

ℓ=1 m6=ℓ
(ν)

As the exponent ν increases the power probabilities p k (x) tend to hard assignments: If M is the index
set of maximal probabilities, and M has #M elements, then,
 1
(ν)
#M , if k ∈ M ;
lim p k (x) =
(32)
ν→∞
0,
otherwise,

8

TSVETAN ASAMOV AND ADI BEN–ISRAEL

and the limit is a hard assignment if #M = 1, i.e. if the maximal probability is unique.
Numerical experience suggests an increase of ν at each iteration, see, e.g., (33) below.
8. Algorithm PCM(ℓ1 ): Probabilistic Clustering Method with ℓ1 distances
The problem (P.K) is solved iteratively, using the following updates in succession.
(ν)

Probabilities computation: Given K centers {c k }, the assignments probabilities {pk (x i )} are calculated using (31). The exponent ν is updated at each iteration, say by a constant increment ∆ ≥ 0,
ν := ν + ∆

(33)

starting with an initial ν0 .
(ν)

Centers computation: Given the assignment probabilities {pk (x i )}, the problem (P.K) separates
into Kn problems of type (29),
min

ck [j]∈R

N
X

wi p

i=1

(ν)

k

(x i ) x i [j] − c k [j] , k ∈ 1,K, j ∈ 1,n,

(34)

one for each component c k [j] of each center c k , that are solved by Lemma 2.
These results are presented in an algorithm form as follows.
Algorithm PCM(ℓ1 ) : An algorithm for the ℓ1 clustering problem
Data:

X = {x i : i ∈ 1,N } data points, {w i : i ∈ 1,N } their weights,
K the number of clusters,
ǫ > 0 (stopping criterion),
ν0 ≥ 1 (initial value of the exponent ν), ∆ > 0 (the increment in (33).)

Initialization: K arbitrary centers {c k : k ∈ 1,K}, ν := ν0 .
Iteration:
Step 1

compute distances {d 1 (x, c k ) : k ∈ 1,K} for all x ∈ X

Step 2

compute the assignments {p

Step 3

compute the new centers {c k+ : k ∈ 1,K} (applying Lemma 2 to (34))

Step 4

if

K
P

d 1 (c k+ , c k ) < ǫ

(ν)

k

(x) : x ∈ X, k ∈ 1,K} (using (31))

stop

k=1

else ν := ν + ∆ , return to step 1

Corollary 1. The running time of Algorithm PCM(ℓ1 ) is
O(N K(K 2 + n)I),

(35)

where n is the dimension of the space, N the number of points, K the number of clusters, and I is the
number of iterations.
Proof. The number of operations in an iteration is calculated as follows:
Step 1: O(nN K), since computing the ℓ1 distance between two n-dimensional vectors takes O(n) time,
and there are N K distances between all points and all centers.
Step 2: O(N K 3 ), there are N K assignments, each taking O(K 2 ).

HIGH DIMENSIONAL CLUSTERING

9

Step 3: O(nN K), computing the weighted median of N points in R takes O(N ) time, and K n such
medians are computed.
Step 4: O(nK), since there are K cluster centers of dimension n.
The corollary is proved by combining the above results.

Remark 1.
(a) The result (35) shows that Algorithm PCM(ℓ1 ) is linear in n, which in high–dimensional data is much
greater than N and K.
(b) The first few iterations of the algorithm come close to the final centers, and thereafter the iterations
are slow, making the stopping rule in Step 4 ineffective. A better stopping rule is a bound on the number
of iterations I, which can then be taken as a constant in (35).
(c) Algorithm PCM(ℓ1 ) can be modified to account for very unequal cluster sizes, as in [14]. This modification did not significantly improve the performance of the algorithm in our experiments.
(d) The centers here are computed from scratch at each iteration using the current probabilities, unlike
the Weiszfeld method [28] or its generalizations, [17]–[18], where the centers are updated at each iteration.
9. Monotonicity
The centers computed iteratively by Algorithm PCM(ℓ1 ) are confined to the convex hull of X, a compact
set, and therefore a subsequence converges to an optimal solution of the approximate problem (P.K), that
in general is not an optimal solution of the original problem (L.K).
The JDF of the data set X is defined as the sum of the JDF’s of its points,
X
D(x).
(36)
D(X) :=
x∈X

We prove next a monotonicity result for D(X).

Theorem 1. The function D(X) decrease along any sequence of iterates of centers.
Proof. The function D(X) can be written as
D(X) :=

X

x∈X

=

K
X

!

pk (x)

k=1

K
X X

D(x), since the probabilities add to 1,

w(x) pk (x)2 d1 (x, c k ), by (21).

(37)

x∈X k=1

The proof is completed by noting that, for each x, the probabilities {pk (x) : k ∈ 1,K} are chosen as to
minimize the function
K
X
w(x) pk (x)2 d1 (x, c k )
(38)
k=1

for the given centers, see (22), and the centers {c k : k ∈ 1,K} minimize the function (38) for the given
probabilities.

Remark 2. The function D(X) also decreases if the exponent ν is increased in (30), for then shorter
distances are becoming more probable in (37).
10. Conclusions
In summary, our approach has the following advantages.
(1) In numerical experiments, see Appendix B, Algorithm PCM(ℓ1 ) outperformed the fuzzy clustering
ℓ1 –method, the K–means ℓ1 method, and the generalized Weiszfeld method [17].

10

TSVETAN ASAMOV AND ADI BEN–ISRAEL

(2) The solutions of (22) are less sensitive to outliers than the solutions of (A-5), which uses squares
of distances.
(3) The probabilistic principle (A-8) allows using other monotonic functions, in particular the exponential function φ(d) = ed , that gives sharper results, and requires only that every distance d(x, c)
be replaced by exp {d(x, c)}, [5].
(4) The JDF (36) of the data set, provides a guide to the “right” number of clusters for the given data,
[6].

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[9] J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, 1981, ISBN 0-306-406713.
[10] J.C. Bezdek and S.K. Pal, (editors), Fuzzy Models for Pattern Recognition: Methods that Search for Structure in Data,
IEEE Press, New York, 1992
[11] B. Chazelle, Finding a good neighbor, near and fast, Comm. ACM 51(2008), 115.
[12] K. R. Dixon and J. A. Chapman, Harmonic mean measure of animal activity areas, Ecology 61(1980), 1040–1044
[13] Z. Drezner, The planar two–center and two–median problems, Transportation Science 18(1984), 351–361.
[14] C. Iyigun and A. Ben–Israel, Probabilistic distance clustering adjusted for cluster size, Probability in Engineering and
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2780.
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SIAM Int. Conf. on Data Mining (2004), 246–257
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[23] MATLAB version 7.14.0.739. Natick, Massachusetts: The MathWorks Inc., 2012.
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8(2007), 65–102.
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355–386.
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and SpatioTemporal Data Mining, pp. 31–45, 2000.
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Appendix A: Relation to previous work

Our work brings together ideas from four different areas: inverse distance weighted interpolation, fuzzy
clustering, subjective probability, and optimality principles.
1. Inverse distance weighted (or IDW) interpolation was introduced in 1965 by Donald Shepard,
who published his results [26] in 1968. Shepard, then an undergraduate at Harvard, worked on the following
problem:
A function u : Rn → R is evaluated at K given points {xk : k ∈ 1,K} in Rn , giving the values
{uk : k ∈ 1,K}, respectively. These values are the only information about the function. It is required to
estimate u at any point x.
Examples of such functions include rainfall in meteorology, and altitude in topography. The point x
cannot be too far from the data points, and ideally lies in their convex hull.
Shepard estimated the value u(x) as a convex combination of the given values uk ,
u(x) =

K
X

λk (x) uk

(A-1)

k=1

where the weights λk (x) are inversely ptoportional to the distances d(x, xk ) between x and xk , say


1
K 

X
 d(x, xk ) 
(A-2)
u(x) =
 uk
 K

P
1
k=1
j=1 d(x, xj )

12

TSVETAN ASAMOV AND ADI BEN–ISRAEL

giving the weights

λk (x) =

Q

d(x, xj )

j6=k
K Q
P

(A-3)
d(x, xm )

ℓ=1 m6=ℓ

that are identical with the probabilities (24), if the data points are identified with the centers. IDW
interpolation is used widely in spatial data analysis, geology, geography, ecology and related areas.
Interpolating the K distances d(x, xk ), i.e. taking uk = d(x, xk ) in (A-2), gives

K

K
Q

d(x, xj )

j=1
K Q
P

(A-4)
d(x, xm )

ℓ=1 m6=ℓ

the harmonic mean of the distances {d(x, xk ) : k ∈ 1,K}, which is the JDF in (25) multiplied by a scalar.

The harmonic mean pops up in several areas of spatial data analysis. In 1980 Dixon and Chapman [12]
posited that the home–range of a species is a contour of the harmonic mean of the areas it frequents, and
this has since been confirmed for hundreds of species. The importance of the harmonic mean in clustering
was established by Teboulle [27], Stanforth, Kolossov and Mirkin [25], Zhang, Hsu, and Dayal [31]–[32],
Ben–Israel and Iyigun [5] and others. Arav [3] showed the harmonic mean of distances to satisfy a system
of reasonable axioms for contour approximation of data.

2. Fuzzy clustering introduced by J.C. Bezdek in 1973, [8], is a relaxation of the original problem,
replacing the hard assignments of points to clusters by soft, or fuzzy, assignments of points simultaneously
to all clusters, the strength of association of x i with the k th cluster is measured by wik ∈ [0, 1].
In the fuzzy c–means (FCM) method [9] the centers {c k } are computed by
min

K
N X
X
i=1 k=1

where the weights xik are updated as

2

wik =

m
wik
kx i − c k k22 ,

1
K
P

j=1



kx i − c k k2
kx i − c j k2

2/m−1 ,

and the centers are then calculated as convex combinations of the data points,


ck =

N 
m 
X
 wik 
 xi . k ∈ 1,K.
 N
P m
i=1
wjk

(A-5)

(A-6)

(A-7)

j=1

2The weights (A-6) are optimal for the problem (A-5) if they are probabilities, i.e. if they are required to add to 1 for every

point xi .

HIGH DIMENSIONAL CLUSTERING

13

The constant m ≥ 1 (the “fuzzifier”) controls he fuzziness of the assignments, which become hard assignments in the limit as m ↓ 1. For m = 1, FCM is the classical K–means method. If m = 2 then the weights
wik are inversely proportional to the square distance kx i − c k k22 , analogously to (21).

Fuzzy clustering is one of the best known, and most widely used, clustering methods. However, it may
need some modification if the data in question is very high–dimensional, see, e.g. [20].

3. Subjective probability. There is some arbitrariness in the choice of the model and the fuzzifier m
in (A-5)–(A-6). In contrast, the probabilities (24) can be justified axiomatically. Using ideas and classical
results ([22], [30]) from subjective probability it is shown in Appendix B that the cluster membership
probabilities pk (x), and distances dk (x), satisfy an inverse relationship, such as,
pk (x) φ(d(x, ck )) = f (x), k ∈ 1,K,

(A-8)

where φ(·) is non–decreasing, and f (x) does not depend on k. In particular, the choice φ(d) = d gives (21),
which works well in practice.
4. Optimality principle. Equation (A-8) is a necessary optimality condition for the problem
(K
)
K
X
X
min
p2 φ(d(x, ck )) :
pk = 1, pk ≥ 0, k ∈ 1,K ,
k=1

(A-9)

k=1

that reduces to (22) for the choice φ(d) = d. This shows the probabilities {pk (x)} of (24) to be optimal,
for the model chosen.
Remark 3. Minimizing a function of squares of probabilities seems unnatural, so a physical analogy may
help. Consider an electric circuit with K resistances {Rk } connected in parallel. A current I through
the circuit splits into K currents, with current Ik through the resistance Rk . These currents solve an
optimization problem (the Kelvin principle)
(K
)
K
X
X
2
min
I k Rk :
Ik = I
(A-10)
I1 ,··· ,IK

k=1

k=1

that is analogous to (22). The optimality condition for (A-10) is Ohm’s law,
Ik Rk = constant
a statement that potential is well defined, and an analog of (21). The equivalent resistance of the circuit,
i.e. the resistance R such that I 2 R is equal to the minimal value in (A-10), is then the JDF (25) with Rj
instead of d(x, c j ) and w = 1.
Appendix B: Numerical Examples
In the following examples we use synthetic data to be clustered into K = 2 clusters. The data consists
of two randomly generated clusters, X1 with N1 points, and X2 with N2 points.
The data points x = (x1 , · · · , xn ) ∈ Rn of cluster Xk are such that all of their components xi , 1 ≤ i ≤ n
are generated by sampling from a distribution Fk with mean µk , k = 1, 2. In cluster X1 we take µ1 = 1,
and in cluster X2 , µ2 = −1.

We ran Algorithm PCM(ℓ1 ), with the parameters ν0 = 1, ∆ = 0.1, and compared its performance with
that of the fuzzy clustering method [9] with the ℓ1 norm, as well as the generalized Weiszfeld algorithm

14

TSVETAN ASAMOV AND ADI BEN–ISRAEL

Method
n = 104 n = 5 · 104 n = 105 n = 5 · 105 n = 106
PCM (ℓ1 )
0.0
0.0
0.0
0.0
0.0
FCM (ℓ1 )
27.1
38.6
24.4
40.9
40.1
K-means (ℓ1 )
28.9
26.8
12.7
22.4
22.9
Gen. Weiszfeld
48.5
48.8
48.0
48.2
47.9
σ = 16 PCM (ℓ1 )
4.3
0.0
0.0
4.7
0.0
FCM (ℓ1 )
41.0
42.1
44.5
43.9
39.5
K-means (ℓ1 )
41.8
35.2
23.7
23.5
23.6
Gen. Weiszfeld
48.0
47.0
48.4
48.6
48.0
σ = 24 PCM (ℓ1 )
42.6
8.8
0.8
4.8
0.0
FCM (ℓ1 )
46.4
45.9
47.5
39.5
45.1
K-means (ℓ1 )
45.5
42.6
35.6
28.0
24.5
Gen. Weiszfeld
47.9
47.8
47.1
48.0
48.2
σ = 32 PCM (ℓ1 )
46.0
42.2
13.4
13.6
0.0
FCM (ℓ1 )
47.4
46.0
44.8
46.0
46.0
K-means (ℓ1 )
46.4
45.7
40.3
36.0
30.7
Gen. Weiszfeld
48.2
48.9
48.5
48.9
47.8
Table 1. Percentages of misclassified data in Example 1
σ
σ=8

of [18] (that uses Euclidean distances), and the ℓ1 –K-Means algorithm [23]. For each method we used a
stopping rule of at most 100 iterations (for Algorithm PCM(ℓ1 ) this replaces Step 4). For each experiment
we record the average percentage of misclassification (a misclassification occurs when a point in X1 is
declared to be in X2 , or vice versa) from 10 independent problems. In examples 1,2,3 we choose the
probability distributions to be Fk = N (µk , σ).
Example 1. In this example the clusters are of equal size, N1 = N2 = 100. Table 1 gives the percentages
of misclassification under the five methods tested, for different values of σ and dimension n.
Example 2. We use N1 = 200 and N2 = 100. Table 2 gives the percentages of misclassifications for
different values of σ and dimension n.
Example 3. In this case N1 = 1000, N2 = 10. The percentages of misclassification are included in Table 3.

In addition to experiments with normal data, we also consider instances with uniform data in Examples
4 and 5. In this case Fk is a uniform distribution with mean µk and support length |supp(Fk )|.
Example 4. We use N1 = 100, N2 = 100. The results are shown in Table 4.
Example 5. In this instance N1 = 200, N2 = 100. The results are shown in Table 5.

HIGH DIMENSIONAL CLUSTERING

15

Method
n = 104 n = 5 · 104 n = 105 n = 5 · 105 n = 106
PCM (ℓ1 )
0.0
0.0
0.0
0.0
0.0
FCM (ℓ1 )
11.9
19.1
25.2
30.1
22.6
K-means (ℓ1 )
20.8
25.9
18.4
31.4
13.6
Gen. Weiszfeld
37.8
37.9
37.2
36.7
36.4
σ = 16 PCM (ℓ1 )
10.4
0.0
0.0
0.0
0.0
FCM (ℓ1 )
37.7
35.6
35.0
36.2
39.4
K-means (ℓ1 )
35.8
32.0
23.6
31.7
14.1
Gen. Weiszfeld
38.0
37.7
35.8
36.6
37.8
σ = 24 PCM (ℓ1 )
44.1
5.9
1.2
0.0
0.0
FCM (ℓ1 )
41.3
37.7
38.9
36.7
34.6
K-means (ℓ1 )
40.3
39.9
32.7
33.3
15.5
Gen. Weiszfeld
36.8
37.7
36.7
36.9
37.2
σ = 32 PCM (ℓ1 )
47.2
38.7
18.5
0.0
0.0
FCM (ℓ1 )
42.3
38.8
37.0
39.7
38.9
K-means (ℓ1 )
41.5
42.9
37.2
36.8
22.6
Gen. Weiszfeld
36.7
36.9
36.0
36.5
37.4
Table 2. Percentages of misclassified data in Example 2
σ
σ=8

σ
Method
n = 103 n = 5 · 103 n = 104 n = 5 · 104 n = 105
σ = 0.4 PCM (ℓ1 )
46.4
41.1
24.1
5.1
0.9
FCM (ℓ1 )
13.4
0.5
0.0
19.5
32.0
K-means (ℓ1 )
37.4
31.6
27.1
36.5
32.6
Gen. Weiszfeld
35.4
36.7
32.6
33.7
38.7
σ = 0.8 PCM (ℓ1 )
47.4
31.4
23.4
5.4
1.8
FCM (ℓ1 )
29.3
9.5
13.0
27.3
37.2
K-means (ℓ1 )
37.5
32.0
27.1
36.4
32.6
Gen. Weiszfeld
30.3
31.6
25.9
27.9
34.5
σ = 1.2 PCM (ℓ1 )
47.3
33.9
26.2
7.7
1.6
FCM (ℓ1 )
36.4
20.8
23.2
31.1
23.9
K-means (ℓ1 )
38.4
32. 2
28.3
36.4
32.6
Gen. Weiszfeld
22.1
23.8
26.8
21.6
25.5
σ = 1.6 PCM (ℓ1 )
47.8
35.4
27.9
9.8
3.6
FCM (ℓ1 )
41.1
27.8
30.0
27.6
24.2
K-means (ℓ1 )
37.6
32.3
28.3
36.4
33.4
Gen. Weiszfeld
23.1
23.2
21.1
25.4
31.6
Table 3. Percentages of misclassified data in Example 3
In all examples Algorithm PCM(ℓ1 ) was unsurpassed and was the clear winner in Examples 1, 2, 4 and
5.
(Tsvetan Asamov) Department of Operations Research and Financial Engineering, Princeton University, 98
Charlton Street, Princeton, NJ 08540, USA
E-mail address: tasamov@princeton.edu
(Adi Ben–Israel) Rutgers Business School, Rutgers University, 100 Rockafeller Road, Piscataway, NJ 08854,
USA
E-mail address: adi.benisrael@gmail.com

16

TSVETAN ASAMOV AND ADI BEN–ISRAEL

Method
n = 104 n = 5 · 104 n = 105 n = 5 · 105 n = 106
PCM (ℓ1 )
0.0
0.0
0.0
0.0
0.0
FCM (ℓ1 )
0.0
0.1
0.3
2.7
0.1
K-means (ℓ1 )
5.0
5.0
4.8
0.0
0.0
Gen. Weiszfeld
0.0
0.0
0.0
0.0
0.0
|supp(F )| = 16 PCM (ℓ1 )
0.0
0.0
0.0
0.0
0.0
FCM (ℓ1 )
8.9
29.1
26.6
21.9
25.6
K-means (ℓ1 )
23.8
25.9
18.0
23.4
17.8
Gen. Weiszfeld
47.0
49.2
46.8
46.2
47.4
|supp(F )| = 24 PCM (ℓ1 )
0.0
0.0
0.0
0.0
0.0
FCM (ℓ1 )
23.6
39.1
20.1
27.8
25.4
K-means (ℓ1 )
32.0
27.2
18.7
23.2
18.1
Gen. Weiszfeld
47.1
47.4
48.0
47.4
47.3
|supp(F )| = 32 PCM (ℓ1 )
0.3
0.0
0.0
0.0
0.0
FCM (ℓ1 )
28.8
39.9
36.6
42.6
38.8
K-means (ℓ1 )
35.7
27.5
19.3
23.4
18.6
Gen. Weiszfeld
48.1
48.0
47.9
47.8
47.9
Table 4. Percentages of misclassified data in Example 4
|supp(F )|
|supp(F )| = 8

Method
n = 104 n = 5 · 104 n = 105 n = 5 · 105 n = 106
PCM (ℓ1 )
0.0
0.0
0.0
0.0
0.0
FCM (ℓ1 )
0.0
10.0
7.2
0.4
0.4
K-means (ℓ1 )
4.9
13.5
0.0
4.9
14.4
Gen. Weiszfeld
0.0
0.0
13.1
0.0
0.0
|supp(F )| = 16 PCM (ℓ1 )
0.0
0.0
0.0
0.0
0.0
FCM (ℓ1 )
30.8
28.0
28.3
14.8
18.3
K-means (ℓ1 )
22.2
31.8
18.4
17.6
32.0
Gen. Weiszfeld
39.2
36.6
35.7
36.7
36.3
|supp(F )| = 24 PCM (ℓ1 )
0.0
0.0
0.0
0.0
0.0
FCM (ℓ1 )
21.0
26.1
30.3
27.5
37.6
K-means (ℓ1 )
32.3
36.3
22.6
18.1
35.1
Gen. Weiszfeld
37.4
38.4
37.6
36.5
37.8
|supp(F )| = 32 PCM (ℓ1 )
1.5
0.0
0.0
0.0
0.0
FCM (ℓ1 )
38.0
35.0
36.5
38.5
33.5
K-means (ℓ1 )
35.1
36.6
23.1
18.6
35.4
Gen. Weiszfeld
39.7
36.0
37.5
40.0
38.0
Table 5. Percentages of misclassified data in Example 5
|supp(F )|
|supp(F )| = 8

