Uplift Modeling aims to detect subgroups in a population with a specific response or reaction to an action taken on the targeted group. In these models, the Treatment set contains objects that have been exposed to some action, such as a marketing campaign or clinical treatment, while in the Control set, they have not. In this study, a novel ARTIFICIAL IMMUNE SYSTEM-based model was designed using an AIRS classifier to solve uplift modeling problems with improved efficiency. In this approach, a predictive model was built for estimating the conditional probability of receiving the desired response from the subpopulation that has taken the action over the relevant probability of the sub-population that has not taken the action. The proposed model was tested on the Hillstorm-visit-w dataset. Experimental results showed a 138 percent improvement in the area under the uplift curve which is a measure to assess an uplift model's performance.

In this paper a novel technique for automatic data clustering based on the ARTIFICIAL IMMUNE ALGORITHM is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been added to the IMMUNE ALGORITHM to provide, also, a soft computing approach for data clustering. This idea leads to proper number of clusters and effective and powerful clustering process without any additional try and error efforts. Also the manual setting of the number of clusters is available in the proposed ALGORITHM (like other unsupervised clustering approaches) after removing the fuzzy controller from the proposed clustering SYSTEM. The method has been tested on the different kinds of the complex ARTIFICIAL data sets and well known benchmarks. The experimental results show that the performance of the proposed technique is much better than the k-means clustering ALGORITHM (as a conventional one), specially for huge data sets with large feature vector dimensions. Furthermore, it is found that the performance of the proposed approach is comparable, sometimes better than the genetic ALGORITHM based clustering technique (as an evolutionary clustering ALGORITHM).

ARTIFICIAL IMMUNE SYSTEM (AIS) is one of the most meta-heuristic ALGORITHMs to solve complex problems. With a large number of data, creating a rapid decision and stable results are the most challenging tasks due to the rapid variation in real world. Clustering technique is a possible solution for overcoming these problems. The goal of clustering analysis is to group similar objects. AIS ALGORITHM can be used in data clustering analysis. Although AIS is able to good display configure of the search space, but determination of clusters of data set directly using the AIS output will be very difficult and costly. Accordingly, in this paper a two-step ALGORITHM is proposed based on AIS ALGORITHM and hierarchical clustering technique. High execution speed and no need to specify the number of clusters are the benefits of the hierarchical clustering technique. But this technique is sensitive to outlier data. So, in the first stage of introduced ALGORITHM using the proposed AIS ALGORITHM, search space was investigated and the configuration space and therefore outlier data are determined. Then in second phase, using hierarchical clustering technique, clusters and their number are determined. Consequently, the first stage of proposed ALGORITHM eliminates the disadvantages of the hierarchical clustering technique, and AIS problems will be resolved in the second stage of the proposed ALGORITHM. In this paper, the proposed ALGORITHM is evaluated and assessed through two metrics that were identified as (i) execution time (ii) Sum of Squared Error (SSE): the average total distance between the center of a cluster with cluster members used to measure the goodness of a clustering structure. Finally, the proposed ALGORITHM has been implemented on a real sample data composed of the earthquake in Iran and has been compared with the similar ALGORITHM titled Improved Ant SYSTEM-based Clustering ALGORITHM (IASC). IASC is based on Ant Colony SYSTEM (ACS) as the meta-heuristics clustering ALGORITHM. It is a fast ALGORITHM and is suitable for dynamic environments. Table 1 shows the results of evaluation. The results showed that the proposed ALGORITHM is able to cover the drawbacks in AIS and hierarchical clustering techniques and the other hand has high precision and acceptable run speed.