How to do web crawling and data mining in C++?
How to perform web crawling and data mining in C?
A web crawler is an automated program that collects information on the Internet. Data mining is the process of extracting valuable information, patterns and knowledge from large amounts of data. In this article, we will learn how to use C language for web scraping and data mining.
Step 1: Set up network requests
First, we need to use C to write code to send HTTP requests to obtain the required data from the target website. We can use C's curl library to achieve this step. The following is a sample code:
#include <curl/curl.h>
#include <iostream>
#include <string>
size_t writeCallback(void* contents, size_t size, size_t nmemb, std::string* output) {
size_t totalSize = size * nmemb;
output->append(static_cast<char*>(contents), totalSize);
return totalSize;
}
int main() {
CURL* curl;
CURLcode res;
std::string output;
curl_global_init(CURL_GLOBAL_DEFAULT);
curl = curl_easy_init();
if (curl) {
curl_easy_setopt(curl, CURLOPT_URL, "https://example.com");
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, writeCallback);
curl_easy_setopt(curl, CURLOPT_WRITEDATA, &output);
res = curl_easy_perform(curl);
if (res != CURLE_OK) {
std::cerr << "curl_easy_perform() failed: " << curl_easy_strerror(res) << std::endl;
}
curl_easy_cleanup(curl);
}
curl_global_cleanup();
std::cout << output << std::endl;
return 0;
}Step 2: Parse HTML and extract data
In step 1, we have obtained the HTML content of the target website. Next, we need to use an HTML parsing library to parse the HTML and extract the required data. There are several popular HTML parsing libraries in C, such as Gumbo, LibXML, and RapidXML. Here, we will use the Gumbo library for parsing.
#include <gumbo.h>
#include <iostream>
#include <string>
void processElement(GumboNode* node) {
if (node->type != GUMBO_NODE_ELEMENT) {
return;
}
GumboAttribute* href;
if (node->v.element.tag == GUMBO_TAG_A &&
(href = gumbo_get_attribute(&node->v.element.attributes, "href"))) {
std::cout << href->value << std::endl;
}
GumboVector* children = &node->v.element.children;
for (size_t i = 0; i < children->length; ++i) {
processElement(static_cast<GumboNode*>(children->data[i]));
}
}
void parseHTML(const std::string& html) {
GumboOutput* output = gumbo_parse(html.c_str());
processElement(output->root);
gumbo_destroy_output(&kGumboDefaultOptions, output);
}
int main() {
std::string html = "<html><body><a href="https://example.com">Link</a></body></html>";
parseHTML(html);
return 0;
}Step 3: Data Mining and Analysis
Once we obtain the data we need, we can use C's various data mining and analysis algorithms to analyze the data. For example, we can use C's machine learning library to perform cluster analysis, classification analysis, predictive analysis, etc.
#include <iostream>
#include <vector>
#include <mlpack/core.hpp>
#include <mlpack/methods/kmeans/kmeans.hpp>
int main() {
arma::mat data = {
{1.0, 1.0},
{2.0, 1.0},
{4.0, 3.0},
{5.0, 4.0}
};
arma::Row<size_t> assignments;
mlpack::kmeans::KMeans<> model(2);
model.Cluster(data, assignments);
std::cout << "Cluster assignments: " << assignments << std::endl;
return 0;
}In the above code example, we used the KMeans algorithm of the mlpack library to perform cluster analysis on the given data set.
Conclusion
By using C to write web crawler and data mining code, we can automatically collect data from the Internet and use various C data mining algorithms for analysis. This approach can help us discover underlying patterns and regularities and derive valuable information from them.
It should be noted that since web crawlers and data mining involve accessing and processing large amounts of data, memory and performance issues, as well as legality and privacy protection issues need to be carefully handled when writing code , to ensure data accuracy and security.
References:
- C curl library documentation: https://curl.se/libcurl/c/
- Gumbo HTML parsing library: https:// github.com/google/gumbo-parser
- mlpack machine learning library: https://www.mlpack.org/
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