03:45

BigDataX: Rare events in big data

02:17

BigDataX: Characteristics of social networks

03:09

BigDataX: Clustering social networks

02:53

BigDataX: Graph model of social networks

03:38

BigDataX: What is data mining?

01:18

BigDataX: What’s coming up?

03:21

BigDataX: Hierarchical clustering

05:08

BigDataX: Introduction to clustering

03:23

BigDataX: K-means

00:45

BigDataX: What's next?

01:18

BigDataX: Power law distributions

02:09

BigDataX: How to do a job in parallel

01:42

BigDataX: Introduction

01:25

BigDataX: Structure of the web

03:59

BigDataX: Introduction to web search

02:28

BigDataX: The basic idea about PageRank

02:38

BigDataX: Matrix-vector multiplication

03:50

BigDataX: Problems to be avoided in PageRank

04:42

BigDataX: Implement wordcount in Java

04:38

BigDataX: Word Importance in collection of documents

04:33

BigDataX: Association rules

03:11

BigDataX: Counting distinct customers

03:27

BigDataX: Four Vs of big data

03:09

BigDataX: Sampling

03:38

BigDataX: A-priori algorithm

01:50

BigDataX: Welcome to BigDataX

02:02

BigDataX: What are similar items and how can we identify them

02:14

BigDataX: Measuring effectiveness

03:25

BigDataX: Content-based systems

04:06

BigDataX: Online advertising

03:24

BigDataX: Random graph and scale-free graph models

01:41

BigDataX: Queries for data streams

03:50

BigDataX: Frequent itemsets

02:38

BigDataX: Locality-Sensitive Hashing (LSH) for documents

03:52

BigDataX: The AdWords problem

01:35

BigDataX: Represent documents as sets

01:52

BigDataX: Build shingles from words

04:14

BigDataX: The balance algorithm

03:08

BigDataX: Collaborative filtering systems

03:21

BigDataX: Matching algorithms to solve the AdWords problem

01:32

BigDataX: Thank you

04:10

BigDataX: Applications

03:13

BigDataX: Bloom filtering

03:39

BigDataX: Types of recommendation systems

05:14

BigDataX: Market-basket model