Lift (data mining) | EPFL Graph Search

In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target (T) is much better than the baseline (B) average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. Mathematically, : \operatorname{lift} = \frac{P(T \mid B)}{P(T)} = \frac{P(T\wedge B)}{P(T)P(B)} For example, suppose a population has an average response rate of 5%, but a certain model (or rule) has identified a segment with a response rate of 20%. Then that segment would have a lift of 4.0 (20%/5%). Applications Typically, the modeller seeks to divide the population into quantiles, and rank the quantiles by lift.

Data Mining and Analysis on Twitter

Sapan Diwakar, Pulkit Goyal

With the tremendous growth of social networks, there has been a growth in the amount of new data that is being created every minute on these networking sites. Twitter acts as a great source of rich information for millions of users on the internet and therefore is apt for applying data mining. The notion of community in this social networking world has caught lots of attention. Such algorithms are even harder to analyse users on Twitter as it is an asymmetric micro blogging service. If you follow me, I do not have to follow you. This means that the connections of Twitter depend less on in-person contact, as many users have more followers than they know. Studying Twitter is useful for understanding how people use new communication technologies to form social connections and maintain existing ones. We begin with a few discussions of how geo-tagged tweets in Twitter can be used to identify useful user features and behaviours as well as identify landmarks/places of interests. We then present an analysis of clustering algorithms and propose different similarity measures to detect communities. We conclude with a brief discussion about different similarity features that affect the event of a future mention between users on Twitter.

2012

OrthoNet: Multilayer Network Data Clustering

Giovanni Chierchia, Mireille El Gheche, Pascal Frossard

Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some information about the network edges. In some cases, this network information can even be given with multiple views or layers, each one representing a different type of relationship between the network nodes. Increasingly often, network nodes also carry a feature vector. We propose in this paper to extend the node clustering problem, that commonly considers only the network information, to a problem where both the network information and the node features are considered together for learning a clustering-friendly representation of the feature space. Specifically, we design a generic two-step algorithm for multilayer network data clustering. The first step aggregates the different layers of network information into a graph representation given by the geometric mean of the network Laplacian matrices. The second step uses a neural net to learn a feature embedding that is consistent with the structure given by the network layers. We propose a novel algorithm for efficiently training the neural net via gradient descent, which encourages the neural net outputs to span the leading eigenvectors of the aggregated Laplacian matrix, in order to capture the pairwise interactions on the network, and provide a clustering-friendly representation of the feature space. We demonstrate with an extensive set of experiments on synthetic and real datasets that our method leads to a significant improvement w.r.t. state-of-the-art multilayer graph clustering algorithms, as it judiciously combines nodes features and network information in the node embedding algorithms.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Thermal and Voltage-Aware Performance Management of 3D MPSoCs with Flow Cell Arrays and Integrated SC Converters

Giovanni Ansaloni, David Atienza Alonso, Alexandre Sébastien Julien Levisse, Halima Najibi, Marina Zapater Sancho

Flow cell arrays (FCAs) concurrently provide efficient on-chip liquid cooling and electrochemical power generation. This technology is especially promising for three-dimensional multi-processor systems-on-chip (3D MPSoCs) realized in deeply scaled technologies, which present very challenging power and thermal requirements. Indeed, FCAs effectively improve power delivery network (PDN) performance, particularly if switched capacitor (SC) converters are employed to decouple the flow cells and the systems-on-chip voltages, allowing each to operate at their optimal point. Nonetheless, the design of FCA-based solutions entails non-obvious considerations and trade-offs, stemming from their dual role in governing both the thermal and power delivery characteristics of 3D MPSoCs. Showcasing them in this paper, we explore multiple FCA design configurations and demonstrate that this technology can decrease the temperature of a heterogeneous 3D MPSoC by 78°C, and its total power consumption by 46%, compared to a high-performance cold-plate based liquid cooling solution. At the same time, FCAs enable up to 90% voltage drop recovery across dies, using SC converters occupying a small fraction of the chip area. Such outcomes provide an opportunity to boost 3D MPSoC computing performance by increasing the operating frequency of dies. Leveraging these results, we introduce a novel temperature and voltage-aware model predictive control (MPC) strategy that optimizes power efficiency during run-time. We achieve application-wide speed-ups of up to 16% on various machine learning (ML), data mining, and other high-performance benchmarks while keeping the 3D MPSoC temperature below 83°C and voltage drops below 5%.

2022