Data Analytics (CS40003) Dr. Debasis Samanta Associate Professor


Comments on k-Means algorithm 5. Complexity analysis of k-Means algorithm



Download 1,76 Mb.
bet8/12
Sana23.07.2022
Hajmi1,76 Mb.
#844889
1   ...   4   5   6   7   8   9   10   11   12
Bog'liq
13ClusteringTechniques

Comments on k-Means algorithm

5. Complexity analysis of k-Means algorithm

Space complexity: The storage complexity can be expressed as follows.

It requires space to store the objects and space to store the proximity measure from objects to the centroids of clusters.

Thus the total storage complexity is

That is, space requirement is in the linear order of if .

  •  

Comments on k-Means algorithm

6. Final comments:

Advantages:

  • k-Means is simple and can be used for a wide variety of object types.
  • It is also efficient both from storage requirement and execution time point of views. By saving distance information from one iteration to the next, the actual number of distance calculations, that must be made can be reduced (specially, as it reaches towards the termination).
  • Limitations:

  • The k-Means is not suitable for all types of data. For example, k-Means does not work on categorical data because mean cannot be defined.
  • k-means finds a local optima and may actually minimize the global optimum.

How similarity metric can be utilized to run k-Means faster? What is the updation in each iteration?
?

Comments on k-Means algorithm

6. Final comments:

Limitations :

  • k-means has trouble clustering data that contains outliers. When the SSE is used as objective function, outliers can unduly influence the cluster that are produced. More precisely, in the presence of outliers, the cluster centroids, in fact, not truly as representative as they would be otherwise. It also influence the SSE measure as well.
  • k-Means algorithm cannot handle non-globular clusters, clusters of different sizes and densities (see Fig 16.6 in the next slide).
  • k-Means algorithm not really beyond the scalability issue (and not so practical for large databases).

Comments on k-Means algorithm


Non-convex shaped clusters
Cluster with different densities
Cluster with different sizes

Download 1,76 Mb.

Do'stlaringiz bilan baham:
1   ...   4   5   6   7   8   9   10   11   12




Ma'lumotlar bazasi mualliflik huquqi bilan himoyalangan ©hozir.org 2024
ma'muriyatiga murojaat qiling

kiriting | ro'yxatdan o'tish
    Bosh sahifa
юртда тантана
Боғда битган
Бугун юртда
Эшитганлар жилманглар
Эшитмадим деманглар
битган бодомлар
Yangiariq tumani
qitish marakazi
Raqamli texnologiyalar
ilishida muhokamadan
tasdiqqa tavsiya
tavsiya etilgan
iqtisodiyot kafedrasi
steiermarkischen landesregierung
asarlaringizni yuboring
o'zingizning asarlaringizni
Iltimos faqat
faqat o'zingizning
steierm rkischen
landesregierung fachabteilung
rkischen landesregierung
hamshira loyihasi
loyihasi mavsum
faolyatining oqibatlari
asosiy adabiyotlar
fakulteti ahborot
ahborot havfsizligi
havfsizligi kafedrasi
fanidan bo’yicha
fakulteti iqtisodiyot
boshqaruv fakulteti
chiqarishda boshqaruv
ishlab chiqarishda
iqtisodiyot fakultet
multiservis tarmoqlari
fanidan asosiy
Uzbek fanidan
mavzulari potok
asosidagi multiservis
'aliyyil a'ziym
billahil 'aliyyil
illaa billahil
quvvata illaa
falah' deganida
Kompyuter savodxonligi
bo’yicha mustaqil
'alal falah'
Hayya 'alal
'alas soloh
Hayya 'alas
mavsum boyicha


yuklab olish