A novel sequence space related to Lp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{L}_{p}$\end{document} defined by Orlicz function with application in pattern recognition

In the field of pattern recognition, clustering groups the data into different clusters on the basis of similarity among them. Many a time, the similarity level between data points is derived through a distance measure; so, a number of clustering techniques reliant on such a measure are developed. Clustering algorithms are modified by employing an appropriate distance measure due to the high versatility of a data set. The distance measure becomes appropriate in clustering algorithm if weights assigned at the components of the distance measure are in concurrence to the problem. In this paper, we propose a new sequence space M(ϕ,p,F)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{{M}} ( \phi,p,\mathcal{{F}} )$\end{document} related to Lp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{L}_{p}$\end{document} using an Orlicz function. Many interesting properties of the sequence space M(ϕ,p,F)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{{M}} ( \phi,p,\mathcal{{F}} )$\end{document} are established by the help of a distance measure, which is also used to modify the k-means clustering algorithm. To show the efficacy of the modified k-means clustering algorithm over the standard k-means clustering algorithm, we have implemented them for two real-world data set, viz. a two-moon data set and a path-based data set (borrowed from the UCI repository). The clustering accuracy obtained by our proposed clustering algoritm outperformes the standard k-means clustering algorithm.


Introduction
Clustering is the process of separating a data set into different groups (clusters) such that objects in the same cluster should be similar to one another but dissimilar in another cluster [-]. It is a procedure to handle unsupervised learning problems appearing in pattern recognition. The major contribution in the field of clustering came due to the pioneering work of MacQueen [] and Bazdek []. The k-means clustering algorithm was introduced by MacQueen [], which is based on the minimum distance of the points from the center. The variants of k-means clustering algorithms were proposed to solve different types of pattern recognitions problems (see [-]). The clustering results of k-means or its variant can be further enhanced by choosing an appropriate distance measure. Therefore, the distance measure has a vital role in the clustering.
Clustering process is usually carried out through the l  distance measure [], but, due to its trajectory, sometimes it fails to offer good results. Suppose that two points x and y are selected on the boundary of the square (case p = ) and let z be the center ( Figure ). Then l  will fail to distinguish x and y, but these points may be distinguished by l  . If the points x and y are on the circumference of the circle, then l  will fail to distinguish them. Moreover, the l p (p ≥ ) distance measures are not flexible, so they cannot be modified as per the need of the clustering problem. Hence, clustering results derived through distance-dependent algorithms basically depend upon two properties of a distance measure: () trajectory and () flexibility. Till now, we have not come across to any distance measure that offers a guaranteed good result for every clustering problems. Clustering is carried out by using other variants of the l p distance measure. The distance measure of the sequence space l p,q ,  ≤ p, q ≤ ∞, introduced by Kellogg  In this paper, we define a new double sequence space M(φ, p, F) related to L p using the following Orlicz function: Obviously, M(φ, p, F) is a norm space, and hence the induced distance measure is represented as The parameters φ, p and Orlicz function F of M(φ, p, F) brings flexibility in the induced distance measure d M(φ,p,F ) , which helps the user to modify it as per need of the clustering problem. Besides, defining the distance measure of M(φ, p, F), we have also studied some of its mathematically established properties. Finally, the distance measure of M(φ, p, F) is used in the k-means clustering algorithm, which clusters real-world data sets such as two-moon data set and path-based data set. The clustering results obtained by the modified clustering algorithm is compared with the k-means clustering algorithm to show its efficacy.

Preliminaries
Throughout the paper, l ∞ , c, and c  denote the Banach spaces of bounded, convergent, and null sequences; ω, N, and R denote the sets of real (ordinary or single) sequences, natural numbers, and real numbers, respectively.

Orlicz function [34]
for all values of x >  and for L > . An Orlicz function F can always be represented in the following integral form: where η, known as the kernel of F , is right-differentiable for t ≥ , η() = , η(t) >  for t > , η is nondecreasing, and η(t) → ∞ as t → ∞.
Let C be the space of finite sets of distinct positive integers. Given any element σ of C. Let c(σ ) be the sequence {c n (σ )} such that c n (σ ) =  if n ∈ σ and c n (σ ) =  otherwise. Further, let be the set of those σ whose support has cardinality at most s, and For ϕ ∈ , the sequence space, introduced by Sargent [] and known as Sargent's sequence space, is defined as follows: Let be the set of all real-valued double sequences, which is a vector space with coordinatewise addition and scalar multiplication. A double sequence x = {x mn } of real numbers is said to be bounded if x ∞ = sup m,n |x mn | < ∞. We denote the space of all bounded double sequences by L ∞ . Consider a sequence x = {x mn } ∈ . If for every ε > , there exist n • = n • (ε) ∈ N and ∈ R such that |x mn -| < ε for all m, n > n • then we say that the double sequence x is convergent in the Pringheim sense to the limit and write Plim x mn = . By C p we denote the space of all convergent double sequences in the Pringsheim sense. It is well known that there are such sequences in the space C p but not in the space L ∞ . So, we can consider the space C bp of double sequences that are both convergent in the Pringsheim sense and bounded, that is, if x is P-convergent to and the limits x m := lim n x m,n (m ∈ N) and x n := lim m x m,n (n ∈ N) exist. Note that, in this case, the limits lim m lim n x m,n and lim n lim m x m,n exist and are equal to the P-limit of x. Therefore, is called the r-limit of x.
In general, for any notion of convergence ν, the space of all ν-convergent double sequences will be denoted by C ν , and the limit of a ν-convergent double sequence x by ν-lim m,n x mn , where ν ∈ {P, bp, r}.
Başar and Sever [] have introduced the space L p of p-summable double sequences corresponding to the space l p (p ≥ ) of single sequences as and examined some properties of the space. Altay and Başar [] have generalized the set of double sequences L ∞ , C p , and C bp etc. by defining L ∞ (t) = ({x mn } ∈ : sup m,n∈N |x mn | t mn < ∞), C p (t) = ({x mn } ∈ : Plim m,n→∞ |x mn -| t mn < ∞), and C bp (t) = C p ∩ L ∞ , respectively, where t = {t mn } is a sequence of strictly positive reals t mn . In the case t mn =  for all m, n ∈ N, L ∞ (t), C p (t), and C bp (t) reduce to the sets L ∞ , C p and C bp , respectively. Now just to have a better idea about other convergences, especially the linear convergence, we first consider the isomorphism defined by Zelster [] as Let λ be the space of double sequences, converging with respect to some linear convergence rule μ-lim : λ → R. The sum of a double series ∞,∞ i,j= x ij with respect to this rule is defined by μ-∞,∞ i,j= x ij := μ-lim s mn . In this paper, we define an analogoue of Sargent's sequence in the double sequence space . For this, we first suppose that U is the space whose elements are finite sets of distinct elements of N × N obtained by σ × ς , where σ ∈ C s and ς ∈ C t for each s, t ≥ . Therefore any element ζ of U means (j, k); j ∈ σ & k ∈ ς having cardinality atmost st, where s is the cardinality with respect to m, and t is the cardinality with respect to n. Here, the product say c of st may be same for differnt sets of positive integers k, l, but in that case, U kl is different from U st . Given any element ζ of U , we denote by c(ζ ) the sequence {c mn (ζ )} such that be the set of those ζ whose support has cardinality at most st, and let For ϕ ∈ , we define the sequence space Throughout the paper, m,n∈ζ means m∈σ n∈ς .
The spaces M(φ, F), L p , and L ∞ can be extended to M(φ, p, F), L p (F ), and L ∞ (F ) as follows: Now, if we take the cardinality t with respect to n as , then M(φ, F) reduce to m(φ, F), and M(φ, p, F) to m(φ, p, F). Here, without further discussing M(ϕ, F), we immediately define M(φ, p, F) so as not to deviate from our main goal to show that M(φ, p, F) is a class of new double sequences lying between L p (F ) and L ∞ (F ). We then further prove certain conditions under which M(φ, p, F) is same as that of L p (F ) and L ∞ (F ). We can easily see that all results in Section  hold for M(φ, F), which is a particular case of M(φ, p, F) with p = .

Some interesting results related to M(φ, p, F ) Theorem . The sequence space M(φ, p, F) is a linear space over R.
Proof Let x, y ∈ M(φ, p, F) and λ, μ ∈ R. Then there exists positive numbers ρ  and ρ  such that Let ρ  = max(|λ|ρ  , |μ|ρ  ). ()  < p < . Using the well-known inequality |a + b| p ≤ |a| p + |b| p for  < p <  and the convexity of Orlicz functions, we have   M(φ, p, F). This proves that M(φ, p, F) is a linear space over R and so obviously is nonempty.
Remark . The distance measure between two sequences x n and y n induced by Ces q p (F ) can be represented as  M(ψ, p, F). M(ψ, p, F) and suppose that sup s,t≥ ( φ st ψ st ) < ∞. Then there exist increasing sequences (s i ) and (t i ) of natural numbers such that lim( φ st ψ st ) = ∞. Now for every b ∈ R + , the set of positive real numbers, there exist i • , j • ∈ N such that Since () holds for all b ∈ R + (we may take b sufficiently large), we have  M(φ, p, F).
Proof Let x ∈ L p (F ). Then, for some ρ > , we have ∞,∞ i,j=, (F ( |x mn | ρ )) p < ∞. Since (ϕ st ) is nondecreasing with respect to s, t ≥ , for some ρ > , we have  p for some A >  and all m, n ∈ N. Thus x ∈ L ∞ (F ).
Let  < ε <  and δ with  < δ <  be such that F(t) < ε,  < t ≤ δ. Put t mn = F  ( |x mn | ρ ) and for any ζ ∈ U s , consider m,n∈ζ where the first sum is over t mn ≤ δ, and the second is over t mn > δ. From the remark we have and for t mn > δ, we use the fact that Since F is nondecreasing and convex, we have Since F satisfies  -condition, we have Hence

k-means algorithm for M(φ, p, F ) distance measure
Let X = {x  , x  , . . . , x n } be a given data set. Then the proposed clustering algoritm works as follows.
Step-: Select first k data points as the cluster center x k = {x  , x  , . . . , x k } (where k is the number of clusters).
Step-: Compute the distance between each data point and cluster center through M(φ, p, F) distance measure.
Step-: Put the data point into that cluster whose M(φ, p, F) distance with its center is minimal.
Step-: Redefine cluster centers for newly evolved clusters due to the above steps; the new cluster centers are computed as c i =  k i k i j= x i , where k i is the number of points in the ith cluster.
Step-: Repeat Step  to Step  until the difference between two consecutive cluster centers becomes less than a desired small number.

Clustering by using the induced M(φ, p, F ) distance measure
Two-moon and path-based data sets are artificially designed as nonconvex collections of points [, ]. The original shapes of the two-moon and path-based data are represented in Figures  and , respectively. The clustering on these two data sets is carried out by the algorithm dissussed in Section .. In the case of a two-moon data set, for making simulation process simple, we take ϕ = , ∀m, n, p = , and F(x) = |x|. In Figure (a), it is shown that the clustering accuracy of the k-means clustering algorithm is % over the two-moon data set, whereas the clustering accuracy of our modefied algorithm k-means    clustering is % (Figure (b)). Moreover, in the case of a path-based data set, we take ϕ = n, ∀m, n, p = , and F(x) = |x|. The clustering accuracy of the path-based data set by using the k-mean clustering algorithm is %, whereas by using the proposed modefied k-means clustering algorithm it is % as shown in Figures (a) and (b), respectively.

Conclusions
The parameters φ, p, F involved in the sequence space M(φ, p, F) give additional three degrees of freedom to its induced distance measure. Therefore, it is more flexible in comparison to the l p or weighted l p distance measure. The flexibility in the distance measure can be judiciously used in the clustering of the real-world data sets. We have proposed only a modified k-means clustering algorithm; in the similar fashion, other distance-based clustering algorithms can also be modified. So, improvement in many clustering algorithms is possible due to a distance measure of M(φ, p, F). We have shown the efficacy of an M(φ, p, F)-based k-means clustering algorithm over the l  -based k-means clustering algorithm on the basis of better clustering accuracy obtained for a two-moon data set and path-based data set.